High-Level Features for Image Indexing and Retrieval

Author:

Ciocca Gianluigi1,Schettini Raimondo1,Cusano Claudio2,Santini Simone3

Affiliation:

1. Università degli Studi di Milano-Bicocca, Italy

2. Università degli Studi di Pavia, Italy

3. Universidad Autónoma de Madrid, Spain

Abstract

Publisher

IGI Global

Reference22 articles.

1. Cai, D., He, X., Li, Z., Ma, W. Y., & Wen, J. R. (2004, October). Hierarchical clustering of WWW image search results using visual, textual and link information. In Proceedings of the 12th annual ACM international conference on Multimedia (pp. 952-959). ACM.

2. Cai, D., He, X., Ma, W. Y., Wen, J. R., & Zhang, H. (2004b, June). Organizing WWW images based on the analysis of page layout and web link structure. In Multimedia and Expo, 2004. ICME'04. 2004 IEEE International Conference on (Vol. 1, pp. 113-116). IEEE.

3. CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines. Circuits and Systems for Video Technology;E.Chang;IEEE Transactions on,2003

4. Cheng, S. F., Chen, W., & Sundaram, H. (1998, October). Semantic visual templates: linking visual features to semantics. In Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on (pp. 531-535). IEEE.

5. Halfway through the semantic gap: Prosemantic features for image retrieval

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