Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
IRM: integrated region matching for image retrieval
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Effects of Different Gabor Filter Parameters on Image Retrieval by Texture
MMM '04 Proceedings of the 10th International Multimedia Modelling Conference
Multiresolution Histograms and Their Use for Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Graph-Theoretic Approach to Nonparametric Cluster Analysis
IEEE Transactions on Computers
A fast MPEG-7 dominant color extraction with new similarity measure for image retrieval
Journal of Visual Communication and Image Representation
Content-Based Image Retrieval with HSV Color Space and Texture Features
WISM '09 Proceedings of the 2009 International Conference on Web Information Systems and Mining
ICSAP '10 Proceedings of the 2010 International Conference on Signal Acquisition and Processing
Series feature aggregation for content-based image retrieval
Computers and Electrical Engineering
Computers and Electrical Engineering
The discrete wavelet transform: wedding the a trous and Mallatalgorithms
IEEE Transactions on Signal Processing
The curvelet transform for image denoising
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
Subspace-based clustering and retrieval of 3-D objects
Computers and Electrical Engineering
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A novel Integrated Curvelet-based image retrieval scheme (ICTEDCT-CBIR) has been proposed, for the purpose of effectively retrieving more similar images from large digital image databases. The proposed model Integrates Curvelet Multiscale ridgelets with Region-based vector codebook Subband Clustering for enhanced dominant colors extraction and texture analysis. An important ingredient of the curvelet transform is to restore sparsity by reducing redundancy across scales. The discrete curvelet transform makes use of a dyadic sequence of scales, and a bank of filters with the property that the pass band filter is concentrated near the frequencies. An enhanced Region-based vector codebook Sub band Clustering (RBSC) has been proposed for effectively extract dominant colors from the color histogram of the transformed image sub-bands. An integrated matching scheme, based on most similar Highest Priority (MSHP) principle, is used to compare the query and target images. Experimental analysis has been carried out to verify the efficiency of the proposed ICTEDCT-CBIR model. Experimental results showed that the proposed approach has better retrieval performance. First, curvelets capture more accurate texture information. Second, as curvelets are tuned to different orientations, it captured more accurate directional features than wavelets. As the experimental results indicated, the proposed technique outperforms other retrieval schemes in terms of average precision with higher precision-recall crossover point values.