Journal Article10.1016/J.ESWA.2011.08.086
Efficient content-based image retrieval using Multiple Support Vector Machines Ensemble
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TL;DR: An extremely fast CBIR system which uses Multiple Support Vector Machines Ensemble is proposed which has used Daubechies wavelet transformation for extracting the feature vectors of images.
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Abstract: Highlights? Effective CBIR for non-texture images. ? An extremely fast CBIR system which uses Multiple Support Vector Machines Ensemble. ? Using Daubechies wavelet transformation for extracting the feature vectors of images. With the evolution of digital technology, there has been a significant increase in the number of images stored in electronic format. These range from personal collections to medical and scientific images that are currently collected in large databases. Many users and organizations now can acquire large numbers of images and it has been very important to retrieve relevant multimedia resources and to effectively locate matching images in the large databases. In this context, content-based image retrieval systems (CBIR) have become very popular for browsing, searching and retrieving images from a large database of digital images with minimum human intervention. The research community are competing for more efficient and effective methods as CBIR systems may be heavily employed in serving time critical applications in scientific and medical domains. This paper proposes an extremely fast CBIR system which uses Multiple Support Vector Machines Ensemble. We have used Daubechies wavelet transformation for extracting the feature vectors of images. The reported test results are very promising. Using data mining techniques not only improved the efficiency of the CBIR systems, but they also improved the accuracy of the overall process.
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An effective hybrid framework for content based image retrieval (CBIR)
TL;DR: In this paper, the authors proposed a CBIR method based on a hybrid features descriptor with the genetic algorithm (GA) and SVM classifier for image retrieval in multi-class scenario, which employed the first three color moments, Haar Wavelet, Daubechies Wavelet and Bi-Orthogonal wavelets for features extraction, refine the features using GA and then train the multi class SVM using one-against-all approach.
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Multimodal Retrieval using Mutual Information based Textual Query Reformulation
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