Content based image retrieval based on Cell Color Coherence Vector (Cell-CCV)

There are a large number of methods in Content Based Image Retrieval (CBIR) systems that are based on color. The Global Color Histogram (GCH) is the most widely used method, the success of which flows from its computational simplicity. The problem with this method, however, is the absence of spatial information on the distribution of the color. To incorporate spatial information, Local Color Histograms (LCHs) are proposed, where, images are decomposed into several blocks. To reduce the space overhead of LCHs, Cell/Color histograms (CCHs) are proposed. In CCH, a cell histogram is calculated for each of the colors present in images. In this paper, we propose a new color based approach called Cell Color Coherence Vector (Cell-CCV). The proposed method is a fusion of two efficient color techniques: Cell Color Histogram (CCH) and Color Coherence Vector (CCV). Indeed, it uses a color coherence vector for each of the colors present in the images. CCV classifies pixels into coherent or incoherent regions. This gives good distinctions that cannot be made with color histograms used in CCH. Therefore, Cell-CCV takes advantage of both methods. The proposed method was tested using Wang dataset (Corel 1K). Results show that the newly developed method (Cell-CCV) performs significantly better than CCV and CCH for image retrieval in terms of weighted precision and average precision of 1000 queries.