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Learning a Sparse Representation for Object Detection

Learning a Sparse Representation for Object Detection,10.1007/3-540-47979-1_8,Shivani Agarwal,Dan Roth

Learning a Sparse Representation for Object Detection   (Citations: 245)
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We present an approach for learning to detect objects in still gray im- ages, that is based on a sparse, part-based representation of objects. A vocabulary of information-rich object parts is automatically constructed from a set of sam- ple images of the object class of interest. Images are then represented using parts from this vocabulary, along with spatial relations observed among them. Based on this representation, a feature-efcient learning algorithm is used to learn to de- tect instances of the object class. The framework developed can be applied to any object with distinguishable parts in a relatively x ed spatial conguration. We report experiments on images of side views of cars. Our experiments show that the method achieves high detection accuracy on a difcult test set of real-world images, and is highly robust to partial occlusion and background variation. In addition, we discuss and offer solutions to several methodological issues that are signicant for the research community to be able to evaluate object detection approaches.
Conference: European Conference on Computer Vision - ECCV , pp. 113-130, 2002
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    • ...Successful methods have been demonstrated in the past, including pedestrian detection [16], general object detection [1, 27] (e.g., vehicles and animals), and scene annotation [17, 24] (e.g., buildings, highways, and social events)...

    Allen Y. Yanget al. Multiple-View Object Recognition in Smart Camera Networks

    • ...During the last decade, parts (or ‘visual words’) have been defined in terms of image patches (Felzenszwalb and Huttenlocher 2005; Moosmann et al. 2006; Shotton et al. 2006), descriptors extracted around interest points (Agrawal and Roth 2002; Fergus et al. 2003 ;L owe2004), edge contours (Fergus et al. 2005; Shotton et al. 2005; Kokkinos et al. 2006 )o r regions (Borenstein and Ullman 2002; Russell et al. 2006)...
    • ...Based on the image structures used to represent parts most approaches can be classified as interest point & descriptorbased (Welling et al. 2000; Agrawal and Roth 2002; Fergus et al. 2003 ;C surka et al.2004; Sivic et al. 2005 ;L ampert et al. 2008), patch- or filter-based (Felzenszwalb and Huttenlocher 2005; Crandall et al. 2005 ;W u et al.2007), contour-based (Fergus et al. 2005; Shotton et al. 2005; Kokkinos et al. 2006; Ferrari et ...
    • ...We validate our method using the UIUC car (Agrawal and Roth 2002) and the ETHZ shape classes (Ferrari et al. 2006)...

    Iasonas Kokkinoset al. Inference and Learning with Hierarchical Shape Models

    • ...Research on detection and localization of faces, cars, motorcycles, pedestrians, road signs, and so on, have been described, and already led to successful and reliable applications [5, 6, 7]. Most techniques are based on the matching of local features in an image to a database of them (a codebook), compiled from a training-set of object images...
    • ...As in [6], the number of true and false positives are then used for computing the precision and recall, and the receiver operator characteristic (ROC), while varying the threshold for detection, as proposed in Section 2.3...

    Arne Robbenet al. Combining object detection and brain computer interfacing: Towards a n...

    • ...There are also previous works in learning visual parts [1, 27] by clustering local patches with spatial configurations...

    Qi Tianet al. Building descriptive and discriminative visual codebook for large-scal...

    • ...We use recall versus (1-Precision) curve [22] by varying global threshold...

    Sungho Kimet al. Robust scale invariant target detection using the scale-space theory a...

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