Content Based Image Retrieval Using Support Vector Machine (SVM)
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Content Based Image Retrieval (CBIR) is a developing trend in Digital Image Processing for searching and retrieving the query image from wide range of databases. Conventional content-based image retrieval (CBIR) schemes have following limitations: 1. It is slow 2. difficult to label negative examples; 3. Accuracy is poor in a single step; we propose a new two- step strategy in which first step is feature extraction using low level features (colour, shape and texture) while SVM classifier is used in the second step to handle the noisy positive examples. Thus, an efficient image retrieval algori...
Content Based Image Retrieval (CBIR) is a developing trend in Digital Image Processing for searching and retrieving the query image from wide range of databases. Conventional content-based image retrieval (CBIR) schemes have following limitations: 1. It is slow 2. difficult to label negative examples; 3. Accuracy is poor in a single step; we propose a new two- step strategy in which first step is feature extraction using low level features (colour, shape and texture) while SVM classifier is used in the second step to handle the noisy positive examples. Thus, an efficient image retrieval algorithm based on color-correlogram for color feature extraction, wavelet transformation for extracting shape features and Gabor wavelet for texture feature extraction.
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