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

Abstract

In this paper, we construct histogram based on Human colour visual perception for Content-Based Image Retrieval. For each pixel, the true colour and grey colour proportion is calculated using a suitable weight function. During histogram construction, the hue and intensity value is iteratively distributed to the neighbouring bins. The NBS distance between the colour value of reference bin to the adjacent bins are estimated. The NBS distance value provides the proportion of overlap of colour of the reference bin with the adjacent bins and accordingly the weight is updated. This kind of procedure for constructing the histogram uses minute colour information and captures the complex background colour content. The distribution makes it possible to extract the background colour information effectively along with the foreground information. The low-level feature of all the database images are extracted and stored in feature database. The relevant images are retrieved for a query image based on the similarity ranking between the query and database images and Manhattan distance is used as similarity measure. The performance of the proposed approach using coral benchmark dataset is encouraging and the precision of retrieval is compared with some of the similar work.

Categorizing Color Appearances of Image Scenes Based on Human Color Perception for Image Retrieval

“…Color distribution in an image can be represented globally as a descriptor in the form of a color histogram [5], [6]. It has been observed that the color histogram approach is the most preferable method in many IR applications [7]- [9]. Color histogram can be generated and calculated through the quantization process of the color model.…”