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Content Based Image Retrieval or CBIR is the image retrieval process which is based on visual features including color, texture and shape. Image databases that are large in size and traditional indexing of images have proven to be less sufficient, more laborious and extremely time consuming which led to its development. Image retrieval and face recognition systems have grown significantly in the area of security systems. Retrieval of images has become an challenging issue in real world applications due to high dimensions of image collection. Therefore it has become a major issue and downfall for Content-Based image Retrieval (CBIR). Furthermore, it results in the inefficiency and degraded classification accuracy. A classifier in combination with the better dimensionality reduction and higher class discrimination can provide higher classification accuracy. This combination must result in a better classification rate. In the field of medical image annotation, research shows that SURF is a very strong tool to be used. Herein, an effort has been formulated to present an efficient algorithm based on SURF which is fast and robust interest point detector, SVM and LDA for further classification. General Terms Feature Extraction, Description, Detection, Classification, Image Retrieval, Dimensionality Reduction.

A Refinement: Better Classification of Images using LDA in Contrast with SURF and SVM for CBIR System