Department of Computer Science
Semantic Image Retrieval Using Collaborative Indexing and Filtering
With the rapid development of multimedia technology, web images have become increasingly available and it constitutes a significant component of multimedia contents on the Internet. Since Web images can be represented in various formats and dimensions, searching such information are far more challenging than text-based search. While some basic forms of image retrieval are available on the Internet, these tend to be inflexible and have significant limitations. Currently, most of these image retrieval systems only rely on shallow image information (e.g., text annotation, metadata, color, etc.). Here, we present an approach for deep concept-based image retrieval, which focuses on high-level human perception, incorporating subtle nuances and emotional impressions on the images. We also propose a user-interactive and innovative adaptive method for image search that overcomes the current limitations. © 2012 IEEE.
collaborative indexing, image retrieval, multimedia search, semantic annotation
Source Publication Title
Proceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops
Link to Publisher's Edition
Leung, C., & Li, Y. (2012). Semantic Image Retrieval Using Collaborative Indexing and Filtering. Proceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, 261-264. https://doi.org/10.1109/WI-IAT.2012.197