Content-based image retrieval (CBIR) is the foundation of image retrieval systems now days. For obtaining more accurate retrieval results, Relevance Feedback (RF) approaches were integrated with CBIR by taking into account the user’s feedbacks information. Trademark recognition and retrieval is a vital application component of Content Based Image Retrieval (CBIR). It deals with matching of the input trademark or logo with stored trademark images in database. This application, under CBIR umbrella, focuses on optimizing search through database by extracting minimum features from set of the images and using relevance feedback mechanism to identify the relevant images. Researchers working in the field of trademark image retrieval have implemented the approaches like quantized representation of the logo/trademark regions, bundling the local features and the features from the spatial neighborhood of trademark images in one unit and learning a statistical model for the distribution of wrong detections.

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