Efficient color image indexing and retrieval using a vector-based scheme

Color is the characteristic which is most used for image indexing and retrieval. Due to its simplicity, the color histogram remains the most commonly used method for color indexing and retrieval. However, the lack of good perceptual histogram similarity measures, the global color content of histograms and the erroneous retrieval results d.ue to gamma nonlinearity, calls for improved methods. implement a vector angular-based distance measure for image retrieval based on color. We build distance vectors in a mult%dimens.ional q t ~ e r y space in which the retrieval ranking of each image is determined. Our system exhibits high flexibility by allowing all types of queries, inclu query by color, query by multiple colors and query by example. In tion, colors can ble excluded in a query, without requiring an additional level of analysis, Content-Based Image Ret,rieval (CBIR) is a, research area dedicated to th.e iinage retrieval problem. Therc are a number of iniagc: and video database systems which have recently bccm developcd and others that are currently u ntler development [ 1 I 21. Color remains the most important lodcvc! feat,ii:.e which is iiscti to build illdices for dataabase images. Specifically, thc: color hist,ogal-n rcrnains the most popular index, due prirmrily to its simplicity [3, 41. However, using the color histogram for indexing has a nrimber of drawbacks. Specifically, histograms rcqiiirc quaxi%ization to reduce dirncnsionality, color space selection can haw a profonrid effect, on t,hc retrieval results and excluding colors in the query is difficult. In this paper we present a scheme for indexing and retrieving color image data, which addresses the drawbacks with histograni techniques and instead implements vector techniques for inclexing and retrieval. We use color segnienta,tion to extract regions of perceptually prominent color and use representative vectors from these extracted regions in the image indices. We end up with a very small index and base similarity on an angular distance measure between a query color vector and the indexed representative vectors. To build indices into our image database we take into consideration factors such as human color perception and recall. Humans describe the color content of an image, with terms such as red or dark yellow, not RGB values. The color granularity provided by histogram indexing is, in most cases, not necessary, especially when the final observer is a human. Thus, it is more natural to segment an image into regions of similar color and retrieve candidate images based on the similarity to the color of that region.