Algorithms for clustering data
Algorithms for clustering data
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrated Region-Based Image Retrieval
Integrated Region-Based Image Retrieval
Cluster validity methods: part I
ACM SIGMOD Record
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
NeTra: a toolbox for navigating large image databases
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
LEGClust—A Clustering Algorithm Based on Layered Entropic Subgraphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multi-prototype clustering algorithm
Pattern Recognition
Multilabel Neighborhood Propagation for Region-Based Image Retrieval
IEEE Transactions on Multimedia
Hybrid Taguchi-genetic algorithm for global numerical optimization
IEEE Transactions on Evolutionary Computation
Questionnaire- versus voice-based screening for laryngeal disorders
Expert Systems with Applications: An International Journal
Feature subset selection using improved binary gravitational search algorithm
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hi-index | 12.05 |
Feature selection plays an important role in image retrieval systems. The better selection of features usually results in higher retrieval accuracy. This work tries to select the best feature set from a total of 78 low level image features, including regional, color, and textual features, using the genetic algorithms (GA). However, the GA is known to be slow to converge. In this work we propose two directions to improve the convergence time of the GA. First we employ the Taguchi method to reduce the number of necessary offspring to be tested in every generation in the GA. Second we propose to use an alternative measure, the Hubert's @C statistics, to evaluate the fitness of each offspring instead of evaluating the retrieval accuracy directly. The experiment results show that the proposed techniques improve the feature selection results by using the GA in both time and accuracy.