Learning image similarities and categories from content analysis and relevance feedback

Authors:
Zijun Yang;C. -C. Jay Kuo
Affiliations:
University of Southern California, 3740 McClintock Avenue, EEB 418, Los Angeles, CA;University of Southern California, 3740 McClintock Avenue, EEB 418, Los Angeles, CA
Venue:
MULTIMEDIA '00 Proceedings of the 2000 ACM workshops on Multimedia
Year:
2000

Citing 6
Cited 1

Powerful image organization in visual retrieval systems

MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
Indoor-Outdoor Image Classification

CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
Categorization of Image Databases for Efficient Retrieval Using Robust Mixture Decomposition

CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Textural Features for Image Database Retrieval

CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Configuration based scene classification and image indexing

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A new approach to image retrieval with hierarchical color clustering

IEEE Transactions on Circuits and Systems for Video Technology

A survey of content-based image retrieval with high-level semantics

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work, a scheme that learns image similarities and categories from relevance feedback is presented. First, we choose the most suitable features to describe images by content analysis and categorize each image by predicting its semantic meanings. During the retrieval process, users are allowed to confirm semantic classification of the query example and evaluate retrieval results with relevance feedback. By analyzing the feedback information, the system learns both image similarities and semantic meanings. In similarity learning, the retrieving results are refined by modifying the similarity metric. Semantic learning is performed by using the decision tree training algorithm.