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

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

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

Content Provider	ACM Digital Library
Author	Nigam, Piyush Tandon, Pradhee Pudi, Vikram Jawahar, C V.
Abstract	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 - Fast Image Search in Huge databases. In FISH, we integrate selected techniques available in the literature, while adding a few of our own. We perform extensive experiments on real datasets to demonstrate the accuracy, efficiency and scalability of FISH. Our results show that the system can easily scale to millions of images while maintaining interactive response time.
Starting Page	369
Ending Page	378
Page Count	10
File Format	PDF
ISBN	9781605580708
DOI	10.1145/1386352.1386400
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-07-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Dynamic indexing Relevance feedback Content extraction Scalability Image retrieval Long term learning Short term learning
Content Type	Text
Resource Type	Article

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