A Study of Order-Based Block Color Feature Image Retrieval Compared with Cumulative Color Histogram Method

Abstract

Color is one of the most important features in image retrieval. Color histograms have proved to be stable representations of an image, but they might be similar in different kinds of images because they describe the global intensity distribution of images. A new color image representation method is proposed in this paper. At first, each color channel (R, G, B) of an image is divided into 48 blocks (6 rows X 8 columns). Secondly, statistical features are computed to characterize the block's color feature. Finally, all block features are combined to form an image's color feature. The experimental results show that the retrieval effectiveness of the proposed technique is better than color histogram-based method. Keywords-order-based block feature; cumulative histograms; image retrieval I.

Human-inspired order-based block feature in the HSI color space for image retrieval

“…Based on the two facts mentioned in section II.A, we put forward a color feature extraction method called Order-based Block Color Feature (OBCF) method [13], it simulates the image classification behavior, and in other words, it has bionic traits.…”

Smooth Weighted Approach for Colour Histogram Construction using Human Colour Perception for CBIR Applications

“…Although it has introduced prior knowledge in the model, the bottom-up attention is only suitable for the primary stage of visual perception, it has limitations. Order-based Block Colour Feature [19] is one type of image's colour feature. It has an advantage of the local colour statistical information but it has some bionic traits that this colour feature cannot alone suffice for CBIR.…”

“…The image is segmented initially using group of circular rings and then the histogram is constructed using the segmented rings. The statistical feature of image blocks has been extracted for representing the colour of blocks, which is named as Order Based Block Colour Feature [19], in this approach image is divided into 48 blocks and the feature is extracted. Since histogram components store the number of pixels having similar colours, it may be considered to be a signature of the complete image represented by a feature vector.…”

A Survey On: Content Based Image Retrieval Systems Using Clustering Techniques For Large Data sets

“…Generally, it adopt histograms to describe it. A color histogram describes the global color distribution in an image and is more frequently used technique for content-based image retrieval (Wang and Qin, 2009) because of its efficiency and effectiveness. Color histograms method has the advantages of speediness, low demand of memory space and not sensitive with the image's change of the size and rotation, it wins extensive attention consequently.…”