Content Based Image Retrieval by Online and Offline
|Swati Killikatt 1, Vidya Kulkarni 2, Madhuri Bijjal3
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With many potential practical applications, content- based image retrieval (CBIR) has attracted substantial attention during the past few years. A variety of relevance feedback (RF) schemes have been developed as a powerful tool to bridge the semantic gap between low-level visual features and high-level semantic concepts, and thus to improve the performance of CBIR systems. Among various RF approaches, support-vector-machine (SVM)-based RF is one of the most popular techniques in CBIR. Despite the success, directly using SVM as an RF scheme has two main drawbacks. First, it treats the positive and negative feedbacks equally, which is not appropriate since the two groups of training feedbacks have distinct properties. Second, as the size of image database increases search and retrieval become slow and it affects the performance of the system. To explore solutions to overcome these two drawbacks, CBIR system is implemented as both offline and online and which make use of the properties of images.