iccv iccv2013 iccv2013-104 iccv2013-104-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Ankit Gandhi, Karteek Alahari, C.V. Jawahar
Abstract: We aim to decompose a global histogram representation of an image into histograms of its associated objects and regions. This task is formulated as an optimization problem, given a set of linear classifiers, which can effectively discriminate the object categories present in the image. Our decomposition bypasses harder problems associated with accurately localizing and segmenting objects. We evaluate our method on a wide variety of composite histograms, and also compare it with MRF-based solutions. In addition to merely measuring the accuracy of decomposition, we also show the utility of the estimated object and background histograms for the task of image classification on the PASCAL VOC 2007 dataset.
[1] B. Alexe, T. Deselaers, and V. Ferrari. What is an object? In CVPR, 2010.
[2] Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. PAMI, 2001.
[3] Y. Chai, V. Lempitsky, and A. Zisserman. BiCoS: A bi-level cosegmentation method for image classification. In ICCV, 2011.
[4] K. Chatfield, V. Lempitsky, A. Vedaldi, and A. Zisserman. The devil is in the details: an evaluation of recent feature encoding methods. In BMVC, 2011.
[5] N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005.
[6] A. Delong, A. Osokin, H. N. Isack, and Y. Boykov. Fast Approximate Energy Minimization with Label Costs. In CVPR, 2010.
[7] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results.
[8] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (VOC) challenge. IJCV, 2010.
[9] P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part based models. PAMI, 2010.
[10] B. Fernando, E. Fromont, and T. Tuytelaars. Effective use of frequent itemset mining for image classification. In ECCV, 2012.
[11] G. Griffin, A. Holub, and P. Perona. Caltech-256 object category dataset. Technical Report 7694, California Institute of Technology, 2007.
[12] V. Kolmogorov. Convergent tree-reweighted message passing for energy minimization. PAMI, 2006.
[13] V. Kolmogorov and R. Zabih. What energy functions can be minimized via graph cuts. PAMI, 2004.
[14] L. Ladicky, C. Russell, P. Kohli, and P. H. S. Torr. Associative hierarchical crfs for object class image segmentation. In ICCV, 2009.
[15] C. H. Lampert, M. B. Blaschko, and T. Hofmann. Beyond sliding windows: Object localization by efficient subwindow search. In CVPR, 2008.
[16] S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In CVPR, 2006.
[17] D. G. Lowe. Distinctive image features from scale-invariant key-
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33] points. IJCV, 2004. S. Maji, A. C. Berg, and J. Malik. Classification using intersection kernel support vector machines is efficient. In CVPR, 2008. A. Oliva and A. Torralba. The role of context in object recognition. Trends in Cognitive Sciences, 2007. M. Pandey and S. Lazebnik. Scene recognition and weakly supervised object localization with deformable part-based models. In ICCV, 2011. O. M. Parkhi, A. Vedaldi, C. V. Jawahar, and A. Zisserman. The truth about cats and dogs. In ICCV, 2011. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988. O. Russakovsky, Y. Lin, K. Yu, and L. Fei-Fei. Object-centric spatial pooling for image classification. In ECCV, 2012. T. Schoenemann. Minimizing count-based high order terms in markov random fields. In EMMCVPR, 2011. G. Sharma, F. Jurie, and C. Schmid. Discriminative spatial saliency for image classification. In CVPR, 2012. J. Shi and J. Malik. Normalized cuts and image segmentation. PAMI, 1997. D. Singaraju and R. Vidal. Using global bag of features models in random fields for joint categorization and segmentation of objects. In CVPR, 2011. J. Sivic, B. Russell, A. Efros, A. Zisserman, and W. Freeman. Discovering objects and their location in images. In ICCV, 2005. J. Sivic and A. Zisserman. Video Google: A text retrieval approach to object matching in videos. In ICCV, 2003. H. O. Song, S. Zickler, T. Althoff, R. Girshick, M. Fritz, C. Geyer, P. Felzenszwalb, and T. Darrell. Sparselet models for efficient multiclass object detection. In ECCV, 2012. A. Torralba, K. P. Murphy, and W. T. Freeman. Sharing visual features for multiclass and multiview object detection. PAMI, 2007. A. Vedaldi and B. Fulkerson. VLFeat: An open and portable library of computer vision algorithms, 2008. A. Vedaldi and A. Zisserman. Efficient additive kernels via explicit feature maps. PAMI, 2011.
[34] J. Verbeek and B. Triggs. Region classification with markov field aspect models. In CVPR, 2007.
[35] J. Winn and J. Shotton. The layout consistent random field for recognizing and segmenting partially occluded objects. In CVPR, 2006.
[36] O. J. Woodford, C. Rother, and V. Kolmogorov. A global perspective on map inference for low-level vision. In ICCV, 2009.
[37] J. Wu. Power mean SVM for large scale visual classification. In CVPR, 2012. 3 12