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A Hybrid Scheme of DDBTC Encoding and CCF,BPF Extraction for Image Retrieval Process with Relevance Feedback

M S Meharban, S. Priya


To overcome the semantic gap in the retrieval process,the user relevance feedback frame work is added to the current image retrieval system. Relevance feedback method iteratively refines and updates the retrieved result by learning the user-labeled examples to further improves the overall performance.In the proposed scheme feature descriptor derived from the DDBTC compressed data stream.Firstly, a color image is decomposed using
DDBTC scheme to produce two new image representations,namely color quantizer and bitmap image by using Improved LBG-VQ (ILBG)algorithm.Two image feature descriptors called Color Histogram Feature (CHF),and Bit Pattern Feature(BPF) can be subsequently generated from the DDBTC color quantizer and its corresponding bitmap image respectively without performing the decoding process.The similarity degree between two images is simply measured with the similarity distance score of their feature descriptor.Second, we presented a relevance feedback (RF) framework for effective image retrieval by using a support vector machine (SVM).Effectiveness of DDBTC feature descriptor is quantitatively examined and compared in the RGB color space as well as in HSI color channel. Extensive experiments shows that there is a significant improvement in retrieval performance with the
proposed method using SVMRF compared with the retrieval performance without RF.


Color Histogram Feature(CHF),Bit Pattern Feature(BPF),Support Vector Machine(SVM),Improved LBG algorithm(ILBG).

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