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.
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.
We present here the results from some of our major experiments while redirect the reader to the
paper for a detailed discussion.
Learning based improvement across feedback iterations over a randomly picked train query
Long term memory based extraction of ROI shown for a few sample images from the database.
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.
Last Modified : Fri May 08, 2008