Image retrieval based on high level concept detection and semantic labelling

Article type: Research Article

Authors: Madduma, Buddhika | Ramanna, Sheela^{; *}

Affiliations: Department of Applied Computer Science, University of Winnipeg, Winnipeg, MB, Canada

Correspondence: [*] Corresponding author: Sheela Ramanna, Department of Applied Computer Science, University of Winnipeg, Winnipeg, MB R3B 2E9, Canada. E-mail: s.ramanna@uwinnipeg.ca

Abstract: This paper presents a novel approach to high-level concept detection and retrieval in images based on a combination of visual thesaurus and multi-class supervised learning. The visual thesaurus includes both conceptual and spatial location information of semantic concepts that are key to image labelling. Our image annotation (or labelling) process includes segmenting and building an image signature. The visual thesaurus is then built using a multi-class supervised SVM classifier. Algorithm for spatial location matching is included. Similarity matching during retrieval is performed on both the content as well as the location information using the standard Euclidean distance. Corel data set was used for experimentation and results were compared with two related approaches to visual thesaurus and image retrieval.

Keywords: Image annotation, concept detection, CBIR, support vector machine, classification, semantic labelling

DOI: 10.3233/IDT-2012-0135

Journal: Intelligent Decision Technologies, vol. 6, no. 3, pp. 187-196, 2012