FISH: Fast Interactive Image Search in Huge Databases


Automated management of the content rich and voluminous multimedia data has attracted tremendous attention over the last decades. Any promising solution should ideally have easy web based access with intuitive querying methods and should be designed to respond with majority of relevant results within a few fractions of a seconds, from amongst millions of real images. This requires the building blocks of the solution like the user interface, representation and indexing, comparison and retrieval, learning and memorization, result presentation etc. all to perform together. Literature abounds with work focusing on small subsets of these blocks but a work unifying the blocks is missing. A practical solution requisites optimal integration all the blocks.

Solution Summary

In FISH we present a complete end-to-end practical system for fast image search in huge databases FISH uses a query-by-example based simplistic interface which maximizes user input. Our proposal uses a set of standard MPEG-7 visual descriptors for representing images. We achieve this with a highly adaptive B+ - tree based indexing scheme which supports the inherent organization of data into similarity clusters. We also adopt a set of popular relevance feedback approaches for incremental short term learning and propose a highly suited scheme for inexpensive and adaptive inter-query or long term learning. We also use the long term learning based long term memory for adaptive improving ROI extraction from images. In our experiments FISH demonstrated commendable retrieval performance from a million real images within fractions of a second.

Some Results

We present here the results from some of our major experiments while redirect the reader to the paper for a detailed discussion.

screen results1 screen results3
Learning based improvement across feedback iterations over a randomly picked train query



chotaTimeline2  chotaTimeline 
  Long term memory based extraction of ROI shown for a few sample images from the database.



Related Publications

  • Pradhee Tandon, Piyush Nigam, Vikram Pudi, C. V. Jawahar - FISH: A Practical System for Fast Interactive Image Search in Huge Databases, Proceedings of the 7th ACM International Conference on Image and Video Retrieval (CIVR '08), July 7-9, 2008, Niagara Falls, Canada.

Associated People




Paper [pdf] Demo