Comparative assessment of efficiency for content based image retrieval systems using different wavelet features and pre-classifier

• Published in: Multimedia tools and applications Vol. 74; no. 24; pp. 11595 - 11630
• Main Authors: Chowdhury, Manish, Kundu, Malay Kumar
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2015; Springer Nature B.V
• Subjects: Algorithms; Assessments; Classification; Classifiers; Clustering; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Digital imaging; Efficiency; Fourier transforms; Fuzzy logic; Image databases; Image retrieval; Information retrieval; Least squares method; Multimedia; Multimedia Information Systems; Performance evaluation; Probability distribution; Representations; Retrieval; Special Purpose and Application-Based Systems; Studies; Transforms; Vector space; Wavelet transforms; Content based image retrieval; Fuzzy C-means clustering algorithm; Multi-resolution analysis; Multi-scale geometric analysis; Classification
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-014-2252-3

Recently, Content Based Image Retrieval (CBIR) has emerged as an active research area having applications in various fields. There exist several states-of-the art CBIR systems that uses both spatial and transform features as input. However, as hardly any details study reported so far on the effectiveness of different transform domain features in CBIR paradigm. This motivates the current article where we have presented extensive comparative assessment of five different transform domain features considering various filter combinations. Three different feature representation schemes and three different classifiers have been used for this purpose. Extensive experiments on four widely used benchmark image databases (Oliva, Caltech101, Caltech256 and MIRFlickr25000) were conducted to determine the best combination of transform, filters, feature representation and classifier. Furthermore, we have also attempted to discover the optimal features from the best combinations using maximal information compression index (MICI). Both qualitative and quantitative evaluations show that the combination of Least Square Support Vector Machine (LSSVM) as a classifier and the statistical parametric framework based reduced feature representation in Non-Subsampled Contourlet Transform (NSCT) with “pyrexc” and “sinc” filters gives the best retrieval performances.