Enhancing Exemplar SVMs using Part Level Transfer Regularization

Content based image retrieval (CBIR), the problem of searching digital images in large databases according to their visual content, is a well established research area in computer vision. In this work we are particularly interested in retrieving subwindows of images which are similar to the given query image, i.e. the goal is detection rather than image level classification. The notion of similarity is defined as being the same object class but also having similar viewpoint (e.g. frontal, left-facing, rear etc.). A query image can be a part of an object (e.g. head of a side facing horse), a complete object (e.g. frontal car image), or a composition of objects (visual phrases, e.g. person riding a horse). For instance, given a query of a horse facing left, the aim is to retrieve any left facing horse (intra-class variation) which might be walking or running with different feet formations (exemplar deformation).

[1]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[2]  Sanja Fidler,et al.  Evaluating multi-class learning strategies in a generative hierarchical framework for object detection , 2009, NIPS.

[3]  Xiao Li,et al.  Regularized adaptation: theory, algorithms and applications , 2007 .

[4]  Barbara Caputo,et al.  The More You Know, the Less You Learn: From Knowledge Transfer to One-shot Learning of Object Categories , 2009, BMVC.

[5]  Rong Yan,et al.  Adapting SVM Classifiers to Data with Shifted Distributions , 2007, Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007).

[6]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  Christoph H. Lampert,et al.  Structured prediction by joint kernel support estimation , 2009, Machine Learning.

[8]  Antonio Torralba,et al.  Sharing features: efficient boosting procedures for multiclass object detection , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[9]  Andrew Zisserman,et al.  Efficient discriminative learning of parts-based models , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[10]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[11]  Rong Yan,et al.  Cross-domain video concept detection using adaptive svms , 2007, ACM Multimedia.

[12]  Giulio Sandini,et al.  Model adaptation with least-squares SVM for adaptive hand prosthetics , 2009, 2009 IEEE International Conference on Robotics and Automation.

[13]  Ali Farhadi,et al.  Describing objects by their attributes , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Ali Farhadi,et al.  Recognition using visual phrases , 2011, CVPR 2011.

[15]  Barbara Caputo,et al.  Safety in numbers: Learning categories from few examples with multi model knowledge transfer , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Ivor W. Tsang,et al.  Visual Event Recognition in Videos by Learning from Web Data , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Andrew W. Fitzgibbon,et al.  Efficient Object Category Recognition Using Classemes , 2010, ECCV.

[18]  Andrew Zisserman,et al.  A Boundary-Fragment-Model for Object Detection , 2006, ECCV.

[19]  Cordelia Schmid,et al.  Combining attributes and Fisher vectors for efficient image retrieval , 2011, CVPR 2011.

[20]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[21]  Michael Goesele,et al.  A shape-based object class model for knowledge transfer , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[22]  Andrew Zisserman,et al.  Tabula rasa: Model transfer for object category detection , 2011, 2011 International Conference on Computer Vision.

[23]  Leonidas J. Guibas,et al.  Human action recognition by learning bases of action attributes and parts , 2011, 2011 International Conference on Computer Vision.

[24]  Alexei A. Efros,et al.  Ensemble of exemplar-SVMs for object detection and beyond , 2011, 2011 International Conference on Computer Vision.

[25]  Mark Everingham,et al.  Shared parts for deformable part-based models , 2011, CVPR 2011.

[26]  Barbara Caputo,et al.  Multiclass transfer learning from unconstrained priors , 2011, 2011 International Conference on Computer Vision.

[27]  Alexei A. Efros,et al.  Data-driven visual similarity for cross-domain image matching , 2011, ACM Trans. Graph..

[28]  Lorenzo Torresani,et al.  Scalable object-class retrieval with approximate and top-k ranking , 2011, 2011 International Conference on Computer Vision.

[29]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.