ICTEDCT-CBIR: Integrating curvelet transform with enhanced dominant colors extraction and texture analysis for efficient content-based image retrieval

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.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► Integrates Curvelet Multiscale ridgelets with vector Subband Clustering. ► Combines spatial band pass filtering at different scales, and orientations. ► Structured dominant Colors with Higher average precision and recall values. ► More accurate directional features are captured, with observable performance gain. ► Retrieval performance is higher than that of Gabor and wavelet-based retrieval.