CiteULike: Group: automatic_summarization - library [278 articles]

SUMMaR: Combining Linguistics and Statistics for Text Summarization

Manfred Stede — 2008-06-05T07:51:34-00:00

Frontiers in Artificial Intelligence and Applications, Vol. 141 (29 August 2006), pp. 827-828.

We describe a text summarization system that moves beyond standard approaches by using a hybrid approach of linguistic and statistical analysis and by employing text-sort-specific knowledge of document structure and phrases indicating importance. The system is highly modular and entirely XML-based so that different components can be combined easily.

Learning Visual Similarity Measures for Comparing Never Seen Objects

Eric Nowak — 2007-08-05T08:24:59-00:00

Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on (2007), pp. 1-8.

In this paper we propose and evaluate an algorithm that learns a similarity measure for comparing never seen objects. The measure is learned from pairs of training images labeled "same" or "different". This is far less informative than the commonly used individual image labels (e.g. "car model X"), but it is cheaper to obtain. The proposed algorithm learns the characteristic differences between local descriptors sampled from pairs of "same" and "different" images. These differences are vector quantized by an ensemble of extremely randomized binary trees, and the similarity measure is computed from the quantized differences. The extremely randomized trees are fast to learn, robust due to the redundant information they carry and they have been proved to be very good clusterers. Furthermore, the trees ef ciently combine different feature types (SIFT and geometry). We evaluate our innovative similarity measure on four very different datasets and consistantly outperform the state-of-the-art competitive approaches.

Proximity Distribution Kernels for Geometric Context in Category Recognition

Haibin Ling — 2007-08-05T05:53:51-00:00

(October 2007)

We propose using the proximity distribution of vector quantized local feature descriptors for object and category recognition. To this end, we introduce a novel “proximity distribution kernel” that naturally combines local geometric as well as photometric information from images. It satisfies Mercer’s condition and can therefore be readily combined with a support vector machine to perform visual categorization in a way that is insensitive to photometric and geometric variations, while retaining significant discriminative power. In particular, it improves on the results obtained both with geometrically un-constrained “bags of features” approaches, as well as with over-constrained “affine procrustes.” Indeed, we test this approach on several challenging data sets, including Graz-01, Graz-02, and the PASCAL challenge, and in all tests it outperforms all previously proposed approaches. We registered the average performance at 91.5% on Graz-01, 82.7% on Graz-02, and 74.5% on PASCAL. Our approach is designed to enforce and exploit geometric consistency among objects in the same category; therefore, it does not improve the performance of existing algorithms on datasets where the data is already roughly aligned and scaled. Our method has the potential to be extended to more complex geometric relationships among local features, as we illustrate in the experiments.

Learning distance functions for image retrieval

T Hertz — 2005-11-09T18:14:24-00:00

Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, Vol. 2 (2004), pp. II-570-II-577 Vol.2.

Image retrieval critically relies on the distance function used to compare a query image to images in the database. We suggest learning such distance functions by training binary classifiers with margins, where the classifiers are defined over the product space of pairs of images. The classifiers are trained to distinguish between pairs in which the images are from the same class and pairs, which contain images from different classes. The signed margin is used as a distance function. We explore several variants of this idea, based on using SVM and boosting algorithms as product space classifiers. Our main contribution is a distance learning method, which combines boosting hypotheses over the product space with a weak learner based on partitioning the original feature space. The weak learner used is a Gaussian mixture model computed using a constrained EM algorithm, where the constraints are equivalence constraints on pairs of data points. This approach allows us to incorporate unlabeled data into the training process. Using some benchmark databases from the UCI repository, we show that our margin based methods significantly outperform existing metric learning methods, which are based an learning a Mahalanobis distance. We then show comparative results of image retrieval in a distributed learning paradigm, using two databases: a large database of facial images (YaleB), and a database of natural images taken from a commercial CD. In both cases our GMM based boosting method outperforms all other methods, and its generalization to unseen classes is superior.

On learning asymmetric dissimilarity measures

K Kummamuru — 2007-06-24T13:09:22-00:00

Data Mining, Fifth IEEE International Conference on (2005), 4 pp..

Many practical applications require that distance measures to be asymmetric and context-sensitive. We introduce context-sensitive learnable asymmetric dissimilarity (CLAD) measures, which are defined to be a weighted sum of a fixed number of dissimilarity measures where the associated weights depend on the point from which the dissimilarity is measured. The parameters used in defining the measure capture the global relationships among the features. We provide an algorithm to learn the dissimilarity measure automatically from a set of user specified comparisons in the form "x is closer to y than to z" and study its performance. The experimental results show that the proposed algorithm outperforms other approaches due to the context sensitive nature of the CLAD measures.

Dynamic visual attention model in image sequences

Mar&\#237;a L&\#243;pez — 2007-06-24T01:58:40-00:00

Image Vision Comput., Vol. 25, No. 5. (May 2007), pp. 597-613.

Text Classification from Labeled and Unlabeled Documents using EM

Kamal Nigam — 2007-03-04T04:25:56-00:00

Machine Learning, Vol. V39, No. 2. (1 May 2000), pp. 103-134.

Bayesian Sets

Z Ghahramani — 2006-12-06T13:41:10-00:00

(2005)

Inspired by "Google Sets", we consider the problem of retrieving items from a concept or cluster, given a query consisting of a few items from that cluster. We formulate this as a Bayesian inference problem and describe a very simple algorithm for solving it. Our algorithm uses a modelbased concept of a cluster and ranks items using a score which evaluates the marginal probability that each item belongs to a cluster containing the query items. For exponential family models with...

A Simple Bayesian Framework for Content-Based Image Retrieval

Katherine Heller — 2006-12-07T12:31:00-00:00

(2006), pp. 2110-2117.

Learning to Locate Informative Features for Visual Identification

Andras Ferencz — 2007-04-03T08:25:51-00:00

International Journal of Computer Vision (2006)

Discriminative Training of Hyper-feature Models for Object Identification

Vidit Jain — 2007-04-03T08:24:34-00:00

(2006)

Learning Hyper-Features for Visual Identification

Andras Ferencz — 2007-04-03T08:20:33-00:00

Vol. 18 (2004)

Building a classification cascade for visual identification from one example

A Ferencz — 2007-04-03T08:12:27-00:00

Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, Vol. 1 (2005), pp. 286-293 Vol. 1.

Object identification (OID) is specialized recognition where the category is known (e.g. cars) and the algorithm recognizes an object's exact identity (e.g. Bob's BMW). Two special challenges characterize OID. (1) Interclass variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. (2) There may be many classes but few or just one positive "training" examples per class. Due to (1), a solution must locate possibly subtle object-specific salient features (a door handle) while avoiding distracting ones (a specular highlight). However, (2) rules out direct techniques of feature selection. We describe an online algorithm that takes one model image from a known category and builds an efficient "same" vs. "different" classification cascade by predicting the most discriminative feature set for that object. Our method not only estimates the saliency and scoring function for each candidate feature, but also models the dependency between features, building an ordered feature sequence unique to a specific model image, maximizing cumulative information content. Learned stopping thresholds make the classifier very efficient. To make this possible, category-specific characteristics are learned automatically in an off-line training procedure from labeled image pairs of the category, without prior knowledge about the category. Our method, using the same algorithm for both cars and faces, outperforms a wide variety of other methods.

Hyperfeatures - Multilevel Local Coding for Visual Recognition

Ankur Agarwal — 2006-04-10T15:37:34-00:00

(2006)

Histograms of local appearance descriptors are a popular representation for visual recognition. They are highly discriminant and they have good resistance to local occlusions and to geometric and photometric variations, but they are not able to exploit spatial co-occurrence statistics of features at scales larger than their local input patches. We present a new multilevel visual representation, `hyperfeatures', that is designed to remedy this. The basis of the work is the familiar notion that to detect object parts, in practice it often suffices to detect co-occurrences of more local object fragments – a process that can be formalized as comparison (vector quantization) of image patches against a codebook of known fragments, followed by local aggregation of the resulting codebook membership vectors to detect co-occurrences. This process converts collections of local image descriptor vectors into slightly less local histogram vectors – higher-level but spatially coarser descriptors. Our central observation is that it can therefore be iterated, and that doing so captures and codes ever larger assemblies of object parts and increasingly abstract or `semantic' image properties. This repeated nonlinear `folding' is essentially different from that of hierarchical models such as Convolutional Neural Networks and HMAX, being based on repeated comparison to local prototypes and accumulation of co-occurrence statistics rather than on repeated convolution and rectification. We formulate the hyperfeatures model and study its performance under several different image coding methods including clustering based Vector Quantization, Gaussian Mixtures, and combinations of these with Latent Discriminant Analysis. We find that the resulting high-level features provide improved performance in several object image and texture image classification tasks.

Using Multiple Segmentations to Discover Objects and their Extent in Image Collections

BC Russell — 2006-11-16T14:33:58-00:00

(2006)

Pictorial Structures for Object Recognition

Pedro Felzenszwalb — 2007-02-28T06:10:54-00:00

International Journal of Computer Vision, Vol. V61, No. 1. (1 January 2005), pp. 55-79.

Unbalanced region matching based on two-level description for image retrieval

Sheng-Yang Dai — 2005-03-11T09:34:09-00:00

Pattern Recognition Letters, Vol. 26, No. 5. (April 2005), pp. 565-580.

Research on integrating spatial information into content-based image retrieval (CBIR) is aimed at solving the problem caused by global feature based algorithm. Most systems derive the spatial information from image segmentation. However, the description of images based on one-level segmentation (OLD) and the inevitable inaccuracy of segmentation results seriously limit the performance. A two-level description (TLD) describes images by a rough description and a detailed description to avoid improper spatial constraint caused by OLD is proposed. Similarity measurement based on unbalanced region matching (URM) is introduced to take advantage of TLD to reduce the influence of segmentation. A novel spatial descriptor integrating shape, size, and density as well as position and spatial layout information together is also proposed. The performance of the integrated system is illustrated by experimental results with 1000 query images randomly selected from a database of 10,000 general-purpose images.

Object class recognition by unsupervised scale-invariant learning

R Fergus — 2007-02-27T10:24:47-00:00

Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on, Vol. 2 (2003), pp. II-264-II-271 vol.2.

We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constellations of parts. A probabilistic representation is used for all aspects of the object: shape, appearance, occlusion and relative scale. An entropy-based feature detector is used to select regions and their scale within the image. In learning the parameters of the scale-invariant object model are estimated. This is done using expectation-maximization in a maximum-likelihood setting. In recognition, this model is used in a Bayesian manner to classify images. The flexible nature of the model is demonstrated by excellent results over a range of datasets including geometrically constrained classes (e.g. faces, cars) and flexible objects (such as animals).

Learning Saliency Maps for Object Categorization

Frank Moosmann — 2007-03-15T10:16:03-00:00

(2006)

Object modeling and recognition using local affine-invariant image descriptors and multi-view spatial contraints

F Rothganger — 2007-03-12T13:44:47-00:00

International Journal of Computer Vision, Vol. 66, No. 3. (2006)

On-Line Construction of Compact Directed Acyclic Word Graphs

Shunsuke Inenaga — 2007-03-12T07:17:04-00:00

Lecture Notes in Computer Science, Vol. 2089 (2001)

Directed Acyclic Word Graph (DAWG) is a space efficient data structure that supports indices of a string. Compact Directed Acyclic Word Graph (CDAWG) is a more space efficient variant of DAWG. Crochemore and Verin gave the first direct algorithm to construct CDAWGs from given strings, based on the McCreight's algorithm for suffix trees. In this paper, we give an Ukkonen's counterpart for CDAWGs. That is, we show an on-line algorithm that constructs CDAWGs from given strings directly.

Fast Practical Multi-Pattern Matching

Maxime Crochemore — 2007-03-12T06:15:04-00:00

Information Processing Letters, Vol. 71, No. 3-4. (1999), pp. 107-113.

The main result of the paper is the construction of a very fast multi-pattern matching algorithm, called DAWG-MATCH. The algorithm is of Boyer-Moore type. Previous algorithm of this type is the Commentz-Walter algorithm. The DAWG-MATCH algorithm behaves better than Commentz-Walter algorithm. We combine the ideas of two algorithms: the Aho-Corasick algorithm, and the Reverse Factor algorithm from Crochemore et alii. The new algorithm performs at most 2jtextj inspections of text characters, and...

Fast text searching for regular expressions or automaton searching on tries

Richardo — 2007-03-11T19:34:51-00:00

Journal of the ACM, Vol. 43, No. 6. (1996), pp. 915-936.

We present algorithms for efficient searching of regular expressions on preprocessed text, using a Patricia tree as a logical model for the index. We obtain searching algorithms which run in logarithmic expected time in the size of the text for a wide subclass of regular expressions, and in sublinear expected time for any regular expression. This is the first such algorithm to be found with this complexity. 1 Introduction Pattern matching and text searching are very important components of...

Multiple Pattern Matching in LZW Compressed Text

Takuya Kida — 2007-03-11T19:25:34-00:00

(1998), pp. 103-112.

In this paper we address the problem of searching in LZW compressed text directly, and present a new algorithm for finding multiple patterns bysimulating the moveofthe Aho-Corasick pattern matching machine. The new algorithm finds all occurrences of multiple patterns whereas the algorithm proposed by Amir, Benson, and Farach finds only the first occurrence of a single pattern.

Shape matching and object recognition using low distortion correspondences

AC Berg — 2006-11-28T09:36:00-00:00

Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, Vol. 1 (2005), pp. 26-33 vol. 1.

We approach recognition in the framework of deformable shape matching, relying on a new algorithm for finding correspondences between feature points. This algorithm sets up correspondence as an integer quadratic programming problem, where the cost function has terms based on similarity of corresponding geometric blur point descriptors as well as the geometric distortion between pairs of corresponding feature points. The algorithm handles outliers, and thus enables matching of exemplars to query images in the presence of occlusion and clutter. Given the correspondences, we estimate an aligning transform, typically a regularized thin plate spline, resulting in a dense correspondence between the two shapes. Object recognition is then handled in a nearest neighbor framework where the distance between exemplar and query is the matching cost between corresponding points. We show results on two datasets. One is the Caltech 101 dataset (Fei-Fei, Fergus and Perona), an extremely challenging dataset with large intraclass variation. Our approach yields a 48% correct classification rate, compared to Fei-Fei et al 's 16%. We also show results for localizing frontal and profile faces that are comparable to special purpose approaches tuned to faces.

Local Features for Object Class Recognition

Krystian Mikolajczyk — 2006-11-28T06:57:43-00:00

(2005), pp. 1792-1799.

Multiclass Object Recognition with Sparse, Localized Features

J Mutch — 2006-11-28T09:34:06-00:00

Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, Vol. 1 (2006), pp. 11-18.

We apply a biologically inspired model of visual object recognition to the multiclass object categorization problem. Our model modifies that of Serre, Wolf, and Poggio. As in that work, we first apply Gabor filters at all positions and scales; feature complexity and position/scale invariance are then built up by alternating template matching and max pooling operations. We refine the approach in several biologically plausible ways, using simple versions of sparsification and lateral inhibition. We demonstrate the value of retaining some position and scale information above the intermediate feature level. Using feature selection we arrive at a model that performs better with fewer features. Our final model is tested on the Caltech 101 object categories and the UIUC car localization task, in both cases achieving state-of-the-art performance. The results strengthen the case for using this class of model in computer vision.

Spatial weighting for bag-of-features

Marcin Marszałek — 2007-03-09T11:27:24-00:00

(2006)

The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects

J Winn — 2007-03-04T15:45:24-00:00

Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, Vol. 1 (2006), pp. 37-44.

This paper addresses the problem of detecting and segmenting partially occluded objects of a known category. We first define a part labelling which densely covers the object. Our Layout Consistent Random Field (LayoutCRF) model then imposes asymmetric local spatial constraints on these labels to ensure the consistent layout of parts whilst allowing for object deformation. Arbitrary occlusions of the object are handled by avoiding the assumption that the whole object is visible. The resulting system is both efficient to train and to apply to novel images, due to a novel annealed layout-consistent expansion move algorithm paired with a randomised decision tree classifier. We apply our technique to images of cars and faces and demonstrate state-of-the-art detection and segmentation performance even in the presence of partial occlusion.

Located Hidden Random Fields: Learning Discriminative Parts for Object Detection

Ashish Kapoor — 2007-03-04T15:41:47-00:00

Vol. 3 (2006)

Object categorization by learned universal visual dictionary

J Winn — 2007-03-04T15:37:13-00:00

Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, Vol. 2 (2005), pp. 1800-1807 Vol. 2.

This paper presents a new algorithm for the automatic recognition of object classes from images (categorization). Compact and yet discriminative appearance-based object class models are automatically learned from a set of training images. The method is simple and extremely fast, making it suitable for many applications such as semantic image retrieval, Web search, and interactive image editing. It classifies a region according to the proportions of different visual words (clusters in feature space). The specific visual words and the typical proportions in each object are learned from a segmented training set. The main contribution of this paper is twofold: i) an optimally compact visual dictionary is learned by pair-wise merging of visual words from an initially large dictionary. The final visual words are described by GMMs. ii) A novel statistical measure of discrimination is proposed which is optimized by each merge operation. High classification accuracy is demonstrated for nine object classes on photographs of real objects viewed under general lighting conditions, poses and viewpoints. The set of test images used for validation comprise: i) photographs acquired by us, ii) images from the Web and iii) images from the recently released Pascal dataset. The proposed algorithm performs well on both texture-rich objects (e.g. grass, sky, trees) and structure-rich ones (e.g. cars, bikes, planes).

LOCUS: learning object classes with unsupervised segmentation

J Winn — 2007-03-04T15:31:34-00:00

Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, Vol. 1 (2005), pp. 756-763 Vol. 1.

We address the problem of learning object class models and object segmentations from unannotated images. We introduce LOCUS (learning object classes with unsupervised segmentation) which uses a generative probabilistic model to combine bottom-up cues of color and edge with top-down cues of shape and pose. A key aspect of this model is that the object appearance is allowed to vary from image to image, allowing for significant within-class variation. By iteratively updating the belief in the object's position, size, segmentation and pose, LOCUS avoids making hard decisions about any of these quantities and so allows for each to be refined at any stage. We show that LOCUS successfully learns an object class model from unlabeled images, whilst also giving segmentation accuracies that rival existing supervised methods. Finally, we demonstrate simultaneous recognition and segmentation in novel images using the learned models for a number of object classes, as well as unsupervised object discovery and tracking in video.

Modeling annotated data

David Blei — 2006-09-06T15:07:16-00:00

(2003), pp. 127-134.

Clustering Appearance and Shape by Learning Jigsaws

Anitha Kannan — 2007-01-30T09:33:40-00:00

Vol. 19 (2006)

Balanced Graph Matching

Timothee Cour — 2007-01-28T07:48:44-00:00

(2006)

SURF: Speeded-Up Robust Features

Herbert Bay — 2006-12-15T13:52:54-00:00

In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness and robustness, yet can be computed and compared much faster. This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper presents experimental results on a standard evaluation set, as well as on imagery obtained in the context of a real-life object recognition application. Both show SURF's strong performance.

Unsupervised Learning of Categories from Sets of Partially Matching Image Features

K Grauman — 2006-11-28T09:38:21-00:00

Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, Vol. 1 (2006), pp. 19-25.

We present a method to automatically learn object categories from unlabeled images. Each image is represented by an unordered set of local features, and all sets are embedded into a space where they cluster according to their partial-match feature correspondences. After efficiently computing the pairwise affinities between the input images in this space, a spectral clustering technique is used to recover the primary groupings among the images. We introduce an efficient means of refining these groupings according to intra-cluster statistics over the subsets of features selected by the partial matches between the images, and based on an optional, variable amount of user supervision. We compute the consistent subsets of feature correspondences within a grouping to infer category feature masks. The output of the algorithm is a partition of the data into a set of learned categories, and a set of classifiers trained from these ranked partitions that can recognize the categories in novel images.

Video Google: a text retrieval approach to object matching in videos

J Sivic — 2006-12-08T04:52:56-00:00

Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on (2003), pp. 1470-1477 vol.2.

We describe an approach to object and scene retrieval which searches for and localizes all the occurrences of a user outlined object in a video. The object is represented by a set of viewpoint invariant region descriptors so that recognition can proceed successfully despite changes in viewpoint, illumination and partial occlusion. The temporal continuity of the video within a shot is used to track the regions in order to reject unstable regions and reduce the effects of noise in the descriptors. The analogy with text retrieval is in the implementation where matches on descriptors are pre-computed (using vector quantization), and inverted file systems and document rankings are used. The result is that retrieved is immediate, returning a ranked list of key frames/shots in the manner of Google. The method is illustrated for matching in two full length feature films.

A Visual Attention Based Region-of-Interest Determination Framework for Video Sequences

Wen-Huang Cheng — 2006-12-07T12:35:09-00:00

IEICE - Trans. Inf. Syst., Vol. E88-D, No. 7. (2005), pp. 1578-1586.

Tiling slideshow

Jun-Cheng Chen — 2006-12-07T12:32:03-00:00

(2006), pp. 25-34.

Automatic video region-of-interest determination based on user attention model

Wen-Huang Cheng — 2006-12-07T12:21:23-00:00

Circuits and Systems, 2005. ISCAS 2005. IEEE International Symposium on (2005), pp. 3219-3222 Vol. 4.

The paper presents a framework for automatic video region-of-interest determination based on a user attention model. A set of attempts on using video attention features and knowledge of applied media aesthetics are made. The three types of visual attention features used are intensity, color, and motion. Referring to aesthetic principles, these features are combined according to camera motion types on the basis of a newly proposed video analysis unit, frame-segment. We conduct subjective experiments on several kinds of video data and demonstrate the effectiveness of the proposed framework.

Visual Attention Detection in Video Sequences Using Spatiotemporal Cues

Yun Zhai — 2006-10-24T17:55:26-00:00

ACM International Conference on Multimedia (ACM MM) (2006)

A Comparison of Affine Region Detectors

K Mikolajczyk — 2006-11-22T16:54:07-00:00

Int. J. Comput. Vision, Vol. 65, No. 1-2. (2005), pp. 43-72.

An efficient parts-based near-duplicate and sub-image retrieval system

Yan Ke — 2006-12-05T07:28:54-00:00

(2004), pp. 869-876.

Scalable Recognition with a Vocabulary Tree

D Nister — 2006-12-05T03:35:14-00:00

Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, Vol. 2 (2006), pp. 2161-2168.

A recognition scheme that scales efficiently to a large number of objects is presented. The efficiency and quality is exhibited in a live demonstration that recognizes CD-covers from a database of 40000 images of popular music CD’s. The scheme builds upon popular techniques of indexing descriptors extracted from local regions, and is robust to background clutter and occlusion. The local region descriptors are hierarchically quantized in a vocabulary tree. The vocabulary tree allows a larger and more discriminatory vocabulary to be used efficiently, which we show experimentally leads to a dramatic improvement in retrieval quality. The most significant property of the scheme is that the tree directly defines the quantization. The quantization and the indexing are therefore fully integrated, essentially being one and the same. The recognition quality is evaluated through retrieval on a database with ground truth, showing the power of the vocabulary tree approach, going as high as 1 million images.

SURF: Speeded Up Robust Features

Herbert Bay — 2006-12-04T13:24:26-00:00

(7-13 May 2006)

In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster. This is achieved by replying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper presents experimental results on a standard evaluation set, as well as on imagery obtained in the context of a real-life object recognition application. Both show SURF's strong performance.

Photo-to-Search: Using Camera Phones to Inquire of the Surrounding World

Menglei Jia — 2006-12-04T12:53:15-00:00

Mobile Data Management, 2006. MDM 2006. 7th International Conference on (2006), pp. 46-46.

With the pervasive use of camera phones, the embedded camera has been considered as a promising HCI manner for mobiles. With necessary technologies, it is possible to become a powerful tool to acquire the information in daily life. We have designed and implemented a system named Photo-to-Search to carry out queries from camera phones simply by taking some photos of interested objects. The captured pictures are compared with a large amount of Web images to select the ones which contain the same prominent object. Consequently, the related information is extracted from the Web pages where the matched images locate. In our demo, data of large buildings, storefronts and products are collected and these kinds of queries are specifically demonstrated to show the efficiency and the effectiveness of our system.

Video retargeting: automating pan and scan

Feng Liu — 2006-12-04T07:19:44-00:00

(2006), pp. 241-250.

Full-frame video stabilization with motion inpainting

Y Matsushita — 2006-11-29T17:08:59-00:00

Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 28, No. 7. (2006), pp. 1150-1163.

Video stabilization is an important video enhancement technology which aims at removing annoying shaky motion from videos. We propose a practical and robust approach of video stabilization that produces full-frame stabilized videos with good visual quality. While most previous methods end up with producing smaller size stabilized videos, our completion method can produce full-frame videos by naturally filling in missing image parts by locally aligning image data of neighboring frames. To achieve this, motion inpainting is proposed to enforce spatial and temporal consistency of the completion in both static and dynamic image areas. In addition, image quality in the stabilized video is enhanced with a new practical deblurring algorithm. Instead of estimating point spread functions, our method transfers and interpolates sharper image pixels of neighboring frames to increase the sharpness of the frame. The proposed video completion and deblurring methods enabled us to develop a complete video stabilizer which can naturally keep the original image quality in the stabilized videos. The effectiveness of our method is confirmed by extensive experiments over a wide variety of videos.

Markov random field modeling in image analysis

Stan Li — 2006-11-25T06:22:13-00:00

(2001)