Using Emergence Phenomenon in Meaningful Image Segmentation for Content-based Image Retrieval

Abstract

Research on multimedia systems and content-based image retrieval has gained momentum during the last decade. Content-based image retrieval (CBIR) is a very difficult area in the access of multimedia databases simply because there still exist vast differences in the perception capacity between a human and a computer. There are two basic problems that still remain unresolved in the area although some progresses have been made [13]. The first one is the problem of efficient and meaningful image segmentation where we break-up a particular image into meaningful parts based on low-level features like color, texture, shape and spatial locations. Developing a segmentation algorithm which will meaningfully segment all images is yet an open problem in image analysis [8]. The second one is the vast gap existing for an image between low-level features mentioned earlier and high-level or semantic expressions contained in the image like the image of a car, a house, a table and so on [11]. To develop efficient indexing techniques for the retrieval of enormous volumes of images being generated these days, we need to achieve reasonable solutions to these abovementioned two problems. But only in very limited and selected cases, some kinds of solutions have been achieved with apparently promising experimental results. In this paper we focus our attention on the first problem. The research identifies few issues causing this gap, for example, failure to capture local image details with low level features, unavailability of semantic representation of images, inadequate human involvement in the retrieval, and ambiguity in query formulation [9]. We offer future directions of research in solving this difficult problem using emergence phenomena. Section one gives an introduction of the area. Section two provides a definition of emergence phenomenon. Section three talks about the use of emergence phenomenon in extracting meanings in image segmentation. Section four suggests future directions of research. We put our concluding remarks in section five.