Abstract
In this chapter, a histogram is constructed based on human colour visual perception for content-based image retrieval. For each pixel, the true colour and grey colour proportion are calculated using a suitable weight function. During histogram construction, the hue and intensity values are iteratively distributed to the neighbouring bins. The NBS distance between the colour values of reference bin to the adjacent bins is estimated. The NBS distance value provides the proportion of the 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 is 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 a similarity measure. The performance of the presented approach using coral benchmark dataset is encouraging, and the precision of retrieval is compared with some of the similar work.
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Shaila, S.G., Vadivel, A. (2018). Smooth Weighted Colour Histogram Using Human Visual Perception for Content-Based Image Retrieval Applications. In: Textual and Visual Information Retrieval using Query Refinement and Pattern Analysis. Springer, Singapore. https://doi.org/10.1007/978-981-13-2559-5_4
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DOI: https://doi.org/10.1007/978-981-13-2559-5_4
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