An Improving Technique of Color Histogram in Segmentation-based Image Retrieval

The distribution of pixel colors in an image generally contains interesting information. Recently, many researchers have analyzed the color attributes of an image and used it as the features of the images for querying. Color histogram is one of the most frequently used image features in the field of color-based image retrieval. The color histogram is widely used as an important color feature indicating the contents of the images in content-based image retrieval (CBIR) systems. Specifically histogram-based algorithms are considered to be effective for color image indexing. Color histogram describes the global distribution of pixels of an image which is insensitive to variations in scale and easy to calculate. However, the high-resolution color histograms are usually high dimension and contain much redundant information which does not relate to the image contents, while the low-resolution histograms can not provide adequate discriminative information for image classification. And an image often includes a part of colors but not all, So there will be many accounts of colors are zeros. In order to save space, we shouldn´t need store them. In this paper, a color high-resolution, non-uniform quantized color histogram is proposed and the improving representation about histogram is proposed too. Major color, major segmentation block, and a new Gray scale co-existing matrixpsilas method are proposed.