Abstract Trademark recognition and retrieval is a vital appliance component of content-based image retrieval (CBIR). Reduction in the semantic gap, attaining more accuracy, reduction in computation complexity, and hence in execution time, are the major challenges in designing and developing a trademark retrieval system. The direction of the proposed work takes into account these challenges by implementing trademark image retrieval through deep convolutional neural networks (DCNNs) integrated with a relevant feedback mechanism. The dataset features are optimized through particle swarm optimization (PSO), reducing the search space. These best/optimized features are given to the self-organizing map (SOM) for clustering at the preprocessing stage. The CNN model is trained on feature representations of relevant and irrelevant images, using the feedback information from the user bringing the marked relevant images closer to the query. Experimentation proved a significant performance when evaluated using FlickrLogos-27, FlickrLogos-32, and FlickrLogos-32 PLUS datasets, as illustrated in the performance results section.

Now adays, content-based image retrieval (CBIR) is the mainstay of image retrieval systems. To be more profitable, Relevance Feedback (RF) techniques were incorporated into CBIR such that more precise results can be obtained by taking user's feedbacks into account. In this paper Content Based Image Retrieval algorithms using Relevance Feedback technique are discussed. The comparative study of these algorithms is done. This article covers various techniques for implementing Content Based Image Retrieval algorithms , their evaluation parameters used and various possible applications of Content Based Image Retrieval algorithms .

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Extraction of certain features from the images is quite difficult to conclude as a result, which can be further used for some kind of specific purpose. In medical field, there is a huge amount of digital images that are related with different diseases. Since, it is a field that demands keen observation of this digital data and responsible work from the concern staff; the chances of error are also very high. This project is an attempt to assistant the medical staff, especially in case of brain tumor to draw a more accurate conclusion on the basis of pattern matching of the captured image with the pre-designed training set. The primary objective of this project is to collaborate the latest available technologies with the knowledge set of the experts to work in a more accurate way. It also aims to serve the man-kind and assist the surgeons in their noble work.

Logo recognition system 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. Obtaining higher accuracy in retrieval process is the main challenge of the work. The retrieval results of CBIR system can be enhanced by using machine learning mechanisms with relevance feedback for Short Term Learning (STL) and Long-Term Learning (LTL). This paper proposes the relevance feedback system embedded with machine learning and optimization technique for logo recognition. Relevance feedback technique is used as baseline model for logo recognition. Feature set is optimized using particle swarm optimization (PSO) and search process is made intelligent by incorporating self-organizing map (SOM). These techniques improve the basic model as depicted in the results.