Feature context for image classification and object detection

In this paper, we presents a new method to encode the spatial information of local image features, which is a natural extension of Shape Context (SC), so we call it Feature Context (FC). Given a position in a image, SC computes histogram of other points belonging to the target binary shape based on their distances and angles to the position. The value of each histogram bin of SC is the number of the shape points in the region assigned to the bin. Thus, SC requires knowing the location of the points of the target shape. In other words, an image point can have only two labels, it belongs to the shape or not. In contrast, FC can be applied to the whole image without knowing the location of the target shape in the image. Each image point can have multiple labels depending on its local features. The value of each histogram bin of FC is a histogram of various features assigned to points in the bin region. We also introduce an efficient coding method to encode the local image features, call Radial Basis Coding (RBC). Combining RBC and FC together, and using a linear SVM classifier, our method is suitable for both image classification and object detection.

[1]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[2]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[3]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[4]  Pietro Perona,et al.  Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[5]  Trevor Darrell,et al.  The pyramid match kernel: discriminative classification with sets of image features , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[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]  Fatih Murat Porikli,et al.  Integral histogram: a fast way to extract histograms in Cartesian spaces , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[8]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[9]  Bernt Schiele,et al.  Cross-Articulation Learning for Robust Detection of Pedestrians , 2006, DAGM-Symposium.

[10]  Jitendra Malik,et al.  SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[11]  Andrew Zisserman,et al.  Incremental learning of object detectors using a visual shape alphabet , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[12]  Cordelia Schmid,et al.  Spatial Weighting for Bag-of-Features , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[13]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[14]  Eli Shechtman,et al.  In defense of Nearest-Neighbor based image classification , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Antonio Torralba,et al.  SIFT Flow: Dense Correspondence across Different Scenes , 2008, ECCV.

[16]  Andrew Zisserman,et al.  Learning an Alphabet of Shape and Appearance for Multi-Class Object Detection , 2008, International Journal of Computer Vision.

[17]  Andrew Blake,et al.  Multiscale Categorical Object Recognition Using Contour Fragments , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Cor J. Veenman,et al.  Kernel Codebooks for Scene Categorization , 2008, ECCV.

[19]  Stefan Roth,et al.  People-tracking-by-detection and people-detection-by-tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Jianbo Shi,et al.  Contour Context Selection for Object Detection: A Set-to-Set Contour Matching Approach , 2008, ECCV.

[21]  Yihong Gong,et al.  Nonlinear Learning using Local Coordinate Coding , 2009, NIPS.

[22]  Shaogang Gong,et al.  Quantifying contextual information for object detection , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[23]  Bernt Schiele,et al.  Pictorial structures revisited: People detection and articulated pose estimation , 2009, CVPR.

[24]  Yihong Gong,et al.  Linear spatial pyramid matching using sparse coding for image classification , 2009, CVPR.

[25]  Zhuowen Tu,et al.  Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Yihong Gong,et al.  Locality-constrained Linear Coding for image classification , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[27]  Changhu Wang,et al.  Spatial-bag-of-features , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[28]  Jianbo Shi,et al.  Many-to-one contour matching for describing and discriminating object shape , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[29]  Thomas S. Huang,et al.  Efficient Highly Over-Complete Sparse Coding Using a Mixture Model , 2010, ECCV.

[30]  Alexander M. Bronstein,et al.  Spatially-Sensitive Affine-Invariant Image Descriptors , 2010, ECCV.

[31]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[32]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .