Shape Similarity Estimation Using Ordinal Measures

In this paper we present a novel approach to shape similarity estimation based on ordinal correlation. The proposed method operates in three steps: object alignment, contour to multilevel image transformation and similarity evaluation. This approach is suitable for use in CBIR. The proposed technique produced encouraging results when applied on the MPEG-7 test data.

[1]  Moncef Gabbouj,et al.  A framework for ordinal-based image correspondence , 2000, 2000 10th European Signal Processing Conference.

[2]  Christos Faloutsos,et al.  QBIC project: querying images by content, using color, texture, and shape , 1993, Electronic Imaging.

[3]  Moncef Gabbouj,et al.  Wavelet-Based Multi-level Object Retrieval In Contour Images , 2000 .

[4]  Roland T. Chin,et al.  On the Detection of Dominant Points on Digital Curves , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Moncef Gabbouj,et al.  A new image similarity measure based on ordinal correlation , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[6]  Moncef Gabbouj,et al.  Content-based description of images for retrieval in large databases: MUVIS , 2000, 2000 10th European Signal Processing Conference.

[7]  Ellen C. Hildreth,et al.  The detection of intensity changes by computer and biological vision systems , 1983, Comput. Vis. Graph. Image Process..

[8]  Jong Beom Ra,et al.  Optimal axes for defining the orientations of shapes , 1996 .

[9]  Josef Kittler,et al.  Curvature scale space image in shape similarity retrieval , 1999, Multimedia Systems.

[10]  Moncef Gabbouj,et al.  MUVIS: a system for content-based indexing and retrieval in large image databases , 1998, Electronic Imaging.

[11]  Rangasami L. Kashyap,et al.  Using Polygons to Recognize and Locate Partially Occluded Objects , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.