A Novel Approach For Improvements In Content Based Image Retrieval
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In CBIR the most common feature used are shape, colors, texture etc. To improve the accuracy of retrieval, it must look on the far side the classical features. The features which could easily be extracted from data could be considered. One of such feature is directionality of the image texture. Directional information can be represented in a compact manner by using transform like wavelet, Gabor, Radon etc. In this book we address this problem of using directional information to increase accuracy of CBIR. Content-based image retrieval (CBIR), additionally called question by image content (QBIC)...
In CBIR the most common feature used are shape, colors, texture etc. To improve the accuracy of retrieval, it must look on the far side the classical features. The features which could easily be extracted from data could be considered. One of such feature is directionality of the image texture. Directional information can be represented in a compact manner by using transform like wavelet, Gabor, Radon etc. In this book we address this problem of using directional information to increase accuracy of CBIR. Content-based image retrieval (CBIR), additionally called question by image content (QBIC) and content-based visual info retrieval (CBVIR) is that the application of laptop vision techniques to the image retrieval drawback, that is, the matter of checking out digital pictures in giant databases. In this book we have compared classical histogram method for image retrieval with retrieval using Gabor, Wavelet, Complex Wavelet, Radon transform and Ridgelet transform. Image retrieval performance is estimated by using Precession and Recall.
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