Hybdrid Content Based Image Retrieval combining multi-objective interactive genetic algorithm and SVM
Abstract
The amount of images contained in repositories or available on Internet has exploded over the last years. In order to retrieve efficiently one or several images in a database, the development of Content-Based Image Retrieval (CBIR) systems has become an intensively active research area. However, most proposed systems are keyword-based and few imply the end-user during the search (through relevance feedback). Visual low-level descriptors are then substituted to keywords but there is a gap between visual description and user expectations. We propose a new framework which combines a multi-objective interactive genetic algorithm, allowing a trade-off between image features and user evaluations, and a support vector machine to learn the user relevance feedback. We test our system on SIMPLIcity database, commonly used in the literature to evaluate CBIR systems using a genetic algorithm, and it outperforms the recent frameworks.
Keywords
Internet
content-based retrieval
feature extraction
interactive systems
relevance feedback
visual databases
CBIR systems
SIMPLIcity database
SVM
hybrid content-based image retrieval
image database
image features
image repository
keyword-based system
multiobjective interactive genetic algorithm
support vector machine
user evaluation
user expectations
user relevance feedback
visual description
visual low-level descriptors
Genetic algorithms
Image retrieval
Sociology
Support vector machines
Training
Vectors