FISH: a practical system for fast interactive image search in huge databases

The problem of search and retrieval of images using relevance feedback has attracted tremendous attention in recent years from the research community. A real-world-deployable interactive image retrieval system must (1) be accurate, (2) require minimal user-interaction, (3) be efficient, (4) be scalable to large collections (millions) of images, and (5) support multi-user sessions. For good accuracy, we need effective methods for learning the relevance of image features based on user feedback, both within a user-session and across sessions. Efficiency and scalability require a good index structure for retrieving results. The index structure must allow for the relevance of image features to continually change with fresh queries and user-feedback. The state-of-the-art methods available today each address only a subset of these issues. In this paper, we build a complete system FISH

• Type: Conference
• Year: 2008
• Where: CIVR
• Authors: Pradhee Tandon, Piyush Nigam, Vikram Pudi, C. V. Jawahar
• Keywords: CIVR 2008, Image Analysis, Images, Relevance Feedback, STORAGE AND RETRIEVAL