Multi-component Models for Object Detection

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• 作者：Chunhui Gu (1) chunhui@google.com
  Pablo Arbeláez (2) arbelaez@eecs.berkeley.edu
  Yuanqing Lin (3) ylin@sv.nec-labs.com
  Kai Yu (4) yukai@baidu.com
  Jitendra Malik (2) malik@eecs.berkeley.edu
• 刊名：Lecture Notes in Computer Science
• 出版年：2012
• 出版时间：2012
• 年：2012
• 卷：7575
• 期：1
• 页码：445-458
• 全文大小：4.9 MB
• 参考文献：1. Alexe, B., Deselaers, T., Ferrari, V.: What is an Object? In: Computer Vision and Pattern Recognition (2010)
  2. Andrews, S., Tsochantaridis, I., Hofmann, T.: Support Vector Machines for Multiple-instance Learning. In: Neural Information Processing Systems (2002)
  3. Arbeláez, P., Hariharan, B., Gu, C., Gupta, S., Bourdev, L., Malik, J.: Semantic Segmentation Using Regions and Parts. In: Computer Vision and Pattern Recognition (2012)
  4. Bourdev, L., Malik, J.: Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations. In: International Conference on Computer Vision (2009)
  5. Bourdev, L., Maji, S., Brox, T., Malik, J.: Detecting People Using Mutually Consistent Poselet Activations. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 168–181. Springer, Heidelberg (2010)
  6. Brox, T., Bourdev, L., Maji, S., Malik, J.: Object Segmentation by Alignment of Poselet Activations to Image Contours. In: Computer Vision and Pattern Recognition (2011)
  7. Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: Computer Vision and Pattern Recognition (2005)
  8. Endres, I., Hoiem, D.: Category Independent Object Proposals. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 575–588. Springer, Heidelberg (2010)
  9. Everingham, M., Van Gool, L., Williams, C., Winn, J., Zisserman, A.: The Pascal Visual Object Classes (VOC) Challenge. International Journal of Computer Vision 88(2), 303–338 (2010)
  10. Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object Detection with Discriminatively Trained Part Based Models. Transactions on Pattern Analysis and Machine Intelligence 32(9), 1627–1645 (2010)
  11. Gu, C., Lim, J., Arbeláez, P., Malik, J.: Recognition Using Regions. In: Computer Vision and Pattern Recognition (2009)
  12. Gu, C., Ren, X.: Discriminative Mixture-of-Templates for Viewpoint Classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 408–421. Springer, Heidelberg (2010)
  13. Lazebnik, S., Schmid, C., Ponce, J.: Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In: Computer Vision and Pattern Recognition (2006)
  14. Li, F., Carreira, J., Sminchisescu, C.: Object Recognition as Ranking Holistic Figure-Ground Hypotheses. In: Computer Vision and Pattern Recognition (2010)
  15. Lin, Y., Cao, L., Lv, F., Zhu, S., Yang, M., Cour, T., Yu, K., Huang, T.: Large-scale Image Classification: Fast Feature Extraction and SVM Training. In: Computer Vision and Pattern Recognition (2011)
  16. Maji, S., Berg, A., Malik, J.: Classification Using Intersection Kernel Support Vector Machines is Efficient. In: Computer Vision and Pattern Recognition (2008)
  17. Malisiewicz, T., Gupta, A., Efros, A.: Ensemble of Exemplar-SVMs for Object Detection and Beyond. In: International Conference on Computer Vision (2011)
  18. Parkhi, O., Vedaldi, A., Jawahar, C., Zisserman, A.: The Truth About Cats and Dogs. In: International Conference on Computer Vision (2011)
  19. Russakovsky, O., Lin, Y., Yu, K., Fei-Fei, L.: Object-Centric Spatial Pooling for Image Classification. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 1–15. Springer, Heidelberg (2012)
  20. van de Sande, K., Gevers, T., Snoek, C.: Evaluating Color Descriptors for Object and Scene Recognition. Transactions on Pattern Analysis and Machine Intelligence 32(9), 1582–1596 (2010)
  21. van de Sande, K., Uijlings, J., Gevers, T., Smeulders, A.: Segmentation as Selective Search for Object Recognition. In: International Conference on Computer Vision (2011)
  22. Vedaldi, A., Fulkerson, B.: VLFeat: An Open and Portable Library of Computer Vision Algorithms (2008), http://www.vlfeat.org/
  23. Yu, K., Zhang, T., Gong, Y.: Nonlinear Learning Using Local Coordinate Coding. In: Neural Information Processing Systems (2009)
  24. Zhu, L., Chen, Y., Yuille, A., Freeman, W.: Latent Hierarchical Structural Learning for Object Detection. In: Computer Vision and Pattern Recognition (2010)
• 作者单位：1. Google Inc., Mountain View, CA, USA2. UC Berkeley, Berkeley, CA, USA3. NEC Labs America, Cupertino, CA, USA4. Baidu Inc., Beijing, China
• ISSN：1611-3349

文摘

In this paper, we propose a multi-component approach for object detection. Rather than attempting to represent an object category with a monolithic model, or pre-defining a reduced set of aspects, we form visual clusters from the data that are tight in appearance and configuration spaces. We train individual classifiers for each component, and then learn a second classifier that operates at the category level by aggregating responses from multiple components. In order to reduce computation cost during detection, we adopt the idea of object window selection, and our segmentation-based selection mechanism produces fewer than 500 windows per image while preserving high object recall. When compared to the leading methods in the challenging VOC PASCAL 2010 dataset, our multi-component approach obtains highly competitive results. Furthermore, unlike monolithic detection methods, our approach allows the transfer of finer-grained semantic information from the components, such as keypoint location and segmentation masks.