Performance Modeling and Algorithm Characterization for Robust Image Segmentation Export

@article{citeulike:3147166, abstract = {Abstract\ \ This paper presents a probabilistic framework based on Bayesian theory for the performance prediction and selection of an optimal segmentation algorithm. The framework models the optimal algorithm selection process as one that accounts for the information content of an input image as well as the behavioral properties of a particular candidate segmentation algorithm. The input image information content is measured in terms of image features while the candidate segmentation algorithm's behavioral characteristics are captured through the use of segmentation quality features. Gaussian probability distribution models are used to learn the required relationships between the extracted image and algorithm features and the framework tested on the Berkeley Segmentation Dataset using four candidate segmentation algorithms.}, author = {Shah, Shishir}, citeulike-article-id = {3147166}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/s11263-008-0130-z}, doi = {10.1007/s11263-008-0130-z}, journal = {International Journal of Computer Vision}, keywords = {algorithm, evaluation, pdf, segmentation}, posted-at = {2008-08-22 08:11:12}, priority = {2}, title = {Performance Modeling and Algorithm Characterization for Robust Image Segmentation}, url = {http://dx.doi.org/10.1007/s11263-008-0130-z} }

TY - JOUR ID - citeulike:3147166 L3 - citeulike-article-id:3147166 N2 - Abstract  This paper presents a probabilistic framework based on Bayesian theory for the performance prediction and selection of an optimal segmentation algorithm. The framework models the optimal algorithm selection process as one that accounts for the information content of an input image as well as the behavioral properties of a particular candidate segmentation algorithm. The input image information content is measured in terms of image features while the candidate segmentation algorithm’s behavioral characteristics are captured through the use of segmentation quality features. Gaussian probability distribution models are used to learn the required relationships between the extracted image and algorithm features and the framework tested on the Berkeley Segmentation Dataset using four candidate segmentation algorithms. JF - International Journal of Computer Vision TI - Performance Modeling and Algorithm Characterization for Robust Image Segmentation KW - algorithm KW - evaluation KW - pdf KW - segmentation AU - Shah, Shishir UR - http://dx.doi.org/10.1007/s11263-008-0130-z ER -