Key Sample Point Selection: An Improvement of Shape Context Algorithm in Image Retrieval

In this work we defined a new algorithm in the field of Content Based Image Retrieval. The Shape Context Algorithm presents a promising solution to the Shape Analysis problem however its use is strongly limited by the high demand of time and space due to the elevated number of Sample Points required. The new algorithm proposed in this study aims to improve the original Shape Context algorithm’s performance modifying some its relevant parts; furthermore, it was evaluated in term of accuracy, computational time and space. The salient aspects of our algorithm are: a new strategy for the Sample Points selection and a center of mass angle approximation technique in the phase of the shape description computation. We want to reduce the number of Sample Points required by the original algorithm in order to attempt to improve the efficiency in real applications.

[1]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[2]  Sven J. Dickinson,et al.  From skeletons to bone graphs: Medial abstraction for object recognition , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Chong-Wah Ngo,et al.  Scale-Rotation Invariant Pattern Entropy for Keypoint-Based Near-Duplicate Detection , 2009, IEEE Transactions on Image Processing.

[4]  James Ze Wang,et al.  Content-based image retrieval: approaches and trends of the new age , 2005, MIR '05.

[5]  Jitendra Malik,et al.  Shape Context: A New Descriptor for Shape Matching and Object Recognition , 2000, NIPS.

[6]  Ulrich Eckhardt,et al.  Shape descriptors for non-rigid shapes with a single closed contour , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[7]  Sameer A. Nene,et al.  Columbia Object Image Library (COIL100) , 1996 .

[8]  Jean Duchon,et al.  Splines minimizing rotation-invariant semi-norms in Sobolev spaces , 1976, Constructive Theory of Functions of Several Variables.

[9]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Mohan M. Trivedi,et al.  3D Shape Context Based Gesture Analysis Integrated with Tracking using Omni Video Array , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[12]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[13]  Jitendra Malik,et al.  Efficient shape matching using shape contexts , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Marcel Körtgen,et al.  3D Shape Matching with 3D Shape Contexts , 2003 .

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

[17]  Serge J. Belongie,et al.  Matching with shape contexts , 2000, 2000 Proceedings Workshop on Content-based Access of Image and Video Libraries.

[18]  G. Wahba Spline models for observational data , 1990 .