Effective Image Representation using Double Colour Histograms for Content-Based Image Retrieval
Ezekiel Mensah Martey, Hang Lei, Xiaoyu Li, Obed Appiah · Informatica · 2021
Image representation is critical to the successful realisation of Content Based Image Retrieval (CBIR) systems. The choice of features to represent the image affects retrieval performance. Nowadays, image databases are heterogeneous, and different feature types can be used for appropriate descriptions. This paper proposes an image representation for CBIR that combines Stacked Colour Histogram (SCH) and Conventional Colour Histogram (CCH) to improve image retrieval precision. This presented technique is designed to capture the colour and texture information of the image. The colour properties of an image are represented by CCH and that of texture by SCH. The weighted similarity measure is used to estimate the proportion of similarity values in the retrieval task. The novel descriptor has been widely tested on four standard image datasets, namely Batik Coil100, Corel10K and Outext. Batik, Coil100 and Outext are used to assess texture discrimination. Corel10K is used to assess the discrimination of heterogeneous images. Experimental results and comparisons with SCH, CMTH, MTH, TCM, CTM and NRFUCTM demonstrate that the proposed descriptor has superior retrieval performance.