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Extended Support Vector Machines for Object Detection and Localization
Feyereisl, Jan,Han, Bo-Hyung The Institute of Electronics and Information Engin 2012 전자공학회지 Vol.39 No.2
Object detection is a fundamental task for many high-level computer vision applications such as image retrieval, scene understanding, activity recognition, visual surveillance and many others. Although object detection is one of the most popular problems in computer vision and various algorithms have been proposed thus far, it is also notoriously difficult, mainly due to lack of proper models for object representation, that handle large variations of object structure and appearance. In this article, we review a branch of object detection algorithms based on Support Vector Machines (SVMs), a well-known max-margin technique to minimize classification error. We introduce a few variations of SVMs-Structural SVMs and Latent SVMs-and discuss their applications to object detection and localization.
Joint Image Clustering and Labeling by Matrix Factorization
Seunghoon Hong,Jonghyun Choi,Feyereisl, Jan,Bohyung Han,Davis, Larry S. IEEE 2016 IEEE transactions on pattern analysis and machine Vol.38 No.7
<P>We propose a novel algorithm to cluster and annotate a set of input images jointly, where the images are clustered into several discriminative groups and each group is identified with representative labels automatically. For these purposes, each input image is first represented by a distribution of candidate labels based on its similarity to images in a labeled reference image database. A set of these label-based representations are then refined collectively through a non-negative matrix factorization with sparsity and orthogonality constraints; the refined representations are employed to cluster and annotate the input images jointly. The proposed approach demonstrates performance improvements in image clustering over existing techniques, and illustrates competitive image labeling accuracy in both quantitative and qualitative evaluation. In addition, we extend our joint clustering and labeling framework to solving the weakly-supervised image classification problem and obtain promising results.</P>