Flexible Shape-Based Query Rewriting

A visual query is based on pictorial representation of conceptual entities and operations. One of the most important features used in visual queries is the shape. Despite its intuitive writing, a shape-based visual query usually suffers of a complexity processing related to two major parameters: 1-the imprecise user request, 2-shapes may undergo several types of transformation. Several methods are provided in the literature to assist the user during query writing. On one hand, relevance feedback technique is widely used to rewrite the initial user query. On the other hand, shape transformations are considered by current shape-based retrieval approaches without any user intervention. In this paper, we present a new cooperative approach based on the shape neighborhood concept allowing the user to rewrite a shape-based visual query according to his preferences with high flexibility in terms of including (or excluding) only some shape transformations and of result sorting.

[1]  Marie-Aude Aufaure-Portier A High Level Interface Language for GIS , 1995 .

[2]  Thomas S. Huang,et al.  Relevance feedback: a power tool for interactive content-based image retrieval , 1998, IEEE Trans. Circuits Syst. Video Technol..

[3]  Mats Carlin,et al.  Measuring the Performance of Shape Similarity Retrieval Methods , 2001, Comput. Vis. Image Underst..

[4]  Bing Yu,et al.  Visual-Language System for User Interfaces , 1995, IEEE Softw..

[5]  John David N. Dionisio,et al.  MQuery: A Visual Query Language for Multimedia, Timeline and Simulation Data , 1996, J. Vis. Lang. Comput..

[6]  Karen Zita Haigh,et al.  Visual Query Language: Finding patterns in and relationships among time series data , 2004 .

[7]  Robert Kohn,et al.  Representation and self-similarity of shapes , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[8]  Tyng-Luh Liu,et al.  Approximate tree matching and shape similarity , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[9]  Isabelle Bloch,et al.  Estimation of Distribution Algorithms: A New Evolutionary Computation Approach for Graph Matching Problems , 2001, EMMCVPR.

[10]  Bernd Meyer,et al.  Beyond Icons: Towards New Metaphors for Visual Query Languages for Spatial Information Systems , 1992 .

[11]  Parke Godfrey,et al.  An overview of cooperative answering , 1992, Journal of Intelligent Information Systems.

[12]  Parke Godfrey,et al.  Relaxation as a platform for cooperative answering , 1992, Journal of Intelligent Information Systems.

[13]  Marie-Aude Aufaure,et al.  LVIS: un langage visuel d'interrogation de bases de données spatiales , 1998, BDA.

[14]  Isabelle Bloch,et al.  Inexact graph matching by means of estimation of distribution algorithms , 2002, Pattern Recognit..

[15]  Wee Kheng Leow,et al.  Perpetual consistency improves image retrieval performance , 2001, SIGIR '01.

[16]  Tyng-Luh Liu,et al.  A generalized shape-axis model for planar shapes , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[17]  Chi-Ren Shyu,et al.  Relevance feedback decision trees in content-based image retrieval , 2000, 2000 Proceedings Workshop on Content-based Access of Image and Video Libraries.

[18]  Sven J. Dickinson,et al.  Skeleton based shape matching and retrieval , 2003, 2003 Shape Modeling International..

[19]  HongJiang Zhang Relevance Feedback in Content-based Image Search , 2001, PRIS.

[20]  Longin Jan Latecki,et al.  Optimal partial shape similarity , 2005, Image Vis. Comput..

[21]  Jayant Shah,et al.  Skeletons of 3D Shapes , 2005, Scale-Space.

[22]  Boaz J. Super,et al.  Improving object recognition accuracy and speed through nonuniform sampling , 2003, SPIE Optics East.