Indexed retrieval by shape appearance

Modern visual information retrieval systems support retrieval by visual content also by directly addressing image visual features such as color, texture, shape and spatial relationships. Combining useful representations and similarity models with efficient index structures is a problem that has been largely underestimated. This problem is particularly challenging in the case of retrieval by shape similarity. In this paper we discuss retrieval by shape similarity, using local features and metric indexing. Shape is partitioned into tokens following curvature analysis. Each token is modeled by a set of perceptually salient attributes and two distinct distance functions are used to model token similarity and shape similarity. Shape indexing is obtained by arranging tokens into a M-tree index structure. Examples from a prototype system an expounded with considerations about the effectiveness of the approach.

[1]  Z. Meral Özsoyoglu,et al.  Distance-based indexing for high-dimensional metric spaces , 1997, SIGMOD '97.

[2]  N GudivadaVenkat,et al.  Design and evaluation of algorithms for image retrieval by spatial similarity , 1995 .

[3]  Raimondo Schettini,et al.  Image Retrieval Using Fuzzy Evaluation of Color Similarity , 1994, Int. J. Pattern Recognit. Artif. Intell..

[4]  Simone Santini,et al.  Similarity Measures , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Rajiv Mehrotra,et al.  Similar-Shape Retrieval in Shape Data Management , 1995, Computer.

[6]  Alberto Del Bimbo,et al.  Visual Image Retrieval by Elastic Matching of User Sketches , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  David A. Forsyth,et al.  Canonical Frames for Planar Object Recognition , 1992, ECCV.

[8]  Alex Pentland,et al.  Photobook: tools for content-based manipulation of image databases , 1994, Electronic Imaging.

[9]  K. Wakimoto,et al.  Efficient and Effective Querying by Image Content , 1994 .

[10]  Antonin Guttman,et al.  R-trees: a dynamic index structure for spatial searching , 1984, SIGMOD '84.

[11]  Del BimboAlberto,et al.  Visual Image Retrieval by Elastic Matching of User Sketches , 1997 .

[12]  Pavel Zezula,et al.  M-tree: An Efficient Access Method for Similarity Search in Metric Spaces , 1997, VLDB.

[13]  Hans-Peter Kriegel,et al.  The R*-tree: an efficient and robust access method for points and rectangles , 1990, SIGMOD '90.

[14]  Shi-Kuo Chang,et al.  Iconic Indexing by 2-D Strings , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Vijay V. Raghavan,et al.  Design and evaluation of algorithms for image retrieval by spatial similarity , 1995, TOIS.

[16]  William I. Grosky,et al.  Index-based object recognition in pictorial data management , 1990, Comput. Vis. Graph. Image Process..

[17]  Fang Liu,et al.  Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Ramesh C. Jain,et al.  Similarity indexing with the SS-tree , 1996, Proceedings of the Twelfth International Conference on Data Engineering.

[19]  Stan Sclaroff,et al.  Deformable prototypes for encoding shape categories in image databases , 1995, Pattern Recognit..

[20]  Benoit Huet,et al.  Fuzzy relational distance for large-scale object recognition , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[21]  Alberto Del Bimbo,et al.  Visual Querying By Color Perceptive Regions , 1998, Pattern Recognit..

[22]  A. Guttmma,et al.  R-trees: a dynamic index structure for spatial searching , 1984 .

[23]  Sergey Brin,et al.  Near Neighbor Search in Large Metric Spaces , 1995, VLDB.

[24]  Toshikazu Kato,et al.  Query by Visual Example - Content based Image Retrieval , 1992, EDBT.

[25]  Anil K. Jain,et al.  Image retrieval using color and shape , 1996, Pattern Recognit..

[26]  Josef Kittler,et al.  Efficient and Robust Retrieval by Shape Content through Curvature Scale Space , 1998, Image Databases and Multi-Media Search.