A memorization learning model for image retrieval

Current image retrieval systems still have major difficulties in bridging the gap between high-level concept and low-level image representation. To overcome these difficulties, a memorization learning model is proposed in this paper. It memorizes the semantic knowledge of images in a database by simply accumulating the user-provided relevance feedback information. From the memorized knowledge, it then learns some hidden semantic information of images. Image retrieval is finally based on a seamless combination of low-level features, memorized semantic information, and estimated hidden semantic information. The model is easy to implement and can be efficiently applied to an image retrieval system. Preliminary experimental results on 10,000 images demonstrate the effectiveness of the proposed model.

[1]  Liu Wenyin,et al.  iFind—a system for semantics and feature based image retrieval over Internet , 2000, MM 2000.

[2]  Ingemar J. Cox,et al.  The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments , 2000, IEEE Trans. Image Process..

[3]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Aya Soffer,et al.  PicASHOW: pictorial authority search by hyperlinks on the web , 2002, ACM Trans. Inf. Syst..

[5]  Thomas S. Huang,et al.  Small sample learning during multimedia retrieval using BiasMap , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[6]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[7]  Bo Zhang,et al.  Support vector machine learning for image retrieval , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[8]  Ilaria Bartolini,et al.  FeedbackBypass: A New Approach to Interactive Similarity Query Processing , 2001, VLDB.

[9]  Mingjing Li,et al.  A statistical correlation model for image retrieval , 2001, MULTIMEDIA '01.

[10]  Wei-Ying Ma,et al.  Information embedding based on user's relevance feedback for image retrieval , 1999, Optics East.

[11]  Jing Huang,et al.  Image indexing using color correlograms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Taher H. Haveliwala Topic-sensitive PageRank , 2002, IEEE Trans. Knowl. Data Eng..