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International Journal of Computer Vision In International Journal of Computer Vision, Vol. 88, No. 2. (1 June 2010), pp. 284-302, doi:10.1007/s11263-009-0271-8 Key: citeulike:5321416
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The goal of this paper is to evaluate and compare models and methods for learning to recognize basic entities in images in an unsupervised setting. In other words, we want to discover the objects present in the images by analyzing unlabeled data and searching for re-occurring patterns. We experiment with various baseline methods, methods based on latent variable models, as well as spectral clustering methods. The results are presented and compared both on subsets of Caltech256 and MSRC2, data sets that are larger and more challenging and that include more object classes than what has previously been reported in the literature. A rigorous framework for evaluating unsupervised object discovery methods is proposed.
This paper proposes an evaluation strategy for object discovery, or unsupervised object detection. The goal of this problem is, given a set of images, identify how locations for objects without explicitly providing bounding boxes. The paper suggest that a common evaluation methodology is required, and conducts several experiments with various models to compare their performance under the proposed methodology. The problem of this approach is that it works on very small data sets, and does not further insights besides the evaluation methodology.
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