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RELEVANCE FEEDBACK FOR CONTENT BASED IMAGE RETRIEVAL BASED ON MULTITEXTON HISTOGRAM AND MICROSTRUCTURE DESCRIPTOR

Image retrieval is an important topic in the field of pattern recognition and artificial intelligence. There are three categories of image retrieval methods: text-based, content-based and semantic-based. In CBIR, images are indexed by their visual content, such as color, texture, shapes. A new image feature detector and descriptor, namely the micro-structure descriptor [1] (MSD) is discussed to describe image features via micro-structures. The micro-structure is defined based on the edge orientation similarity, and the MSD is built based on the underlying colors in micro-structures with similar edge orientation. Content-based image retrieval (CBIR) is the mainstay of image retrieval systems. To be more profitable relevance feedback techniques are incorporated into CBIR such that more precise results can be obtained by taking user‟s feedbacks into account. The semantic gap between low-level features and high-level concepts handled by the user is one of the main problems in image retrieval. On the other hand, the relevance feedback has been used on many CBIR systems such as an effective solution to reduce the semantic gap. The gap is reduced by using the Multitexton Histogram descriptor [2]. In this paper, a novel framework method called Relevance Feedback is used to achieve high efficiency and effectiveness of CBIR in coping with the large-scale image data. For that reason this paper proposes a method of relevance feedback based on Multitexton Histogram descriptor to represents the effective feature representations, and the Microstructure descriptor (MSD) for efficient feature extraction of an image. By using this method, high quality of image retrieval on Relevance Feedback can be achieved in a small number of feedbacks. In terms of efficiency, iteration of feedback is reduced substantially by using the navigation patterns discovered from the user query log, which reduce the computational processing time.