Vector approximation based indexing for non-uniform high dimensional data sets
Proceedings of the ninth international conference on Information and knowledge management
Fast Evaluation Techniques for Complex Similarity Queries
Proceedings of the 27th International Conference on Very Large Data Bases
Learning to cluster web search results
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Image Retrieval from the World Wide Web: Issues, Techniques, and Systems
ACM Computing Surveys (CSUR)
Cortina: a system for large-scale, content-based web image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Intuitive and effective interfaces for WWW image search engines
Proceedings of the 12th annual ACM international conference on Multimedia
Learning No-Reference Quality Metric by Examples
MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
A fast shot matching strategy for detecting duplicate sequences in a television stream
Proceedings of the 2nd international workshop on Computer vision meets databases
Hi-index | 0.00 |
Although content-based image retrieval has been researched for many years, few content-based methods are implemented in present image search engines. This is partly bacause of the great difficulty in indexing and searching in high-dimensional feature space for large-scale image datasets. In this paper, we propose a novel method to represent the content of each image as one or multiple hash codes, which can be considered as special keywords. Based on this compact representation, images can be accessed very quickly by their visual content. Furthermore, two advanced functionalities are implemented. One is content-based image clustering, which is simplified as grouping images with identical or near identical hash codes. The other is content-based similarity search, which is approximated by finding images with similar hash codes. The hash code extraction process is very simple, and both image clustering and similarity search can be performed in real time. Experiments on over 11 million images collected from the web demonstrate the efficiency and effectiveness of the proposed method.