Retrieving Semantic Image Using Shape Descriptors and Latent-Dynamic Conditional Random Fields

Abstract

Abstract This paper introduces a new approach to semantic image retrieval using shape descriptors as dispersion and moment in conjunction with discriminative classifier model of latent-dynamic conditional random fields (LDCRFs). The target region is firstly localized via the background subtraction model. Then the features of dispersion and moments are employed to k-means clustering to extract object’s feature as second stage. After that, the learning process is carried out by LDCRFs. Finally, simple protocol and RDF (resource description framework) query language (i.e. SPARQL) on input text or image query is to retrieve semantic image based on sequential processes of query engine, matching module and ontology manager. Experimental findings show that our approach can be successful to retrieve images against the mammal’s benchmark with retrieving rate of 98.11%. Such outcomes are likely to compare very positively with those accessible in the literature from other researchers.