Improving Performance of Image Retrieval Based on Fuzzy Colour Histograms by Using Hybrid Colour Model and Genetic Algorithm

Simple item page
dc.contributor.authorLjubovic, V.en_US
dc.contributor.authorSupic, H.en_US
dc.contributor.editorDeussen, Oliver and Zhang, Hao (Richard)en_US
dc.date.accessioned2016-01-25T14:31:25Z
dc.date.available2016-01-25T14:31:25Z
dc.date.issued2015en_US
dc.description.abstractA hybrid colour model is a colour descriptor formed by combining channels from several different colour models. Although rarely used in computer graphics applications due to redundancy, hybrid colour models may be of interest for the Content‐Based Image Retrieval (CBIR). Best features of each colour model can be combined to obtain optimal retrieval performance. This paper evaluates several approaches to the construction of a hybrid colour model that is used to construct a fuzzy colour histogram of image as a compact feature for retrieval. By evaluating each channel separately, a colour model named HSY is proposed. Various parameters of fuzzy histogram are further improved using Genetic algorithm (GA). Using standard data sets and the Average Normalized Modified Retrieval Rank (ANMRR) as a metric for retrieval performance, it is shown that this novel approach can give an improved retrieval performance.A hybrid colour model is a colour descriptor formed by combining channels from several different colour models. Although rarely used in computer graphics applications due to redundancy, hybrid colour models may be of interest for the Content‐Based Image Retrieval (CBIR). Best features of each colour model can be combined to obtain optimal retrieval performance. This paper evaluates several approaches to the construction of a hybrid colour model that is used to construct a fuzzy colour histogram of image as a compact feature for retrieval. By evaluating each channel separately, a colour model named HSY is proposed. Various parameters of fuzzy histogram are further improved using Genetic algorithm (GA). Using standard data sets and the Average Normalized Modified Retrieval Rank (ANMRR) as a metric for retrieval performance, it is shown that this novel approach can give an improved retrieval performance.en_US
dc.description.number8en_US
dc.description.sectionheadersArticlesen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume34en_US
dc.identifier.doi10.1111/cgf.12609en_US
dc.identifier.urihttp://dx.doi.org/10.1111/cgf.12609en_US
dc.publisherCopyright © 2015 The Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectimage databasesen_US
dc.subjectgenetic algorithmsen_US
dc.subjectH.2.4 [Database Management]: Systems—Multi‐media databasesen_US
dc.subjectH.3.3 [Information Storage and Retrieval]: Information Search and Retreival—Retreival modelsen_US
dc.subjectI.4.7 [Image Processing and Computer Vision]: Feature Measurement—Feature representationen_US
dc.subjectInvariantsen_US
dc.titleImproving Performance of Image Retrieval Based on Fuzzy Colour Histograms by Using Hybrid Colour Model and Genetic Algorithmen_US
Files
Collections
34-Issue 8