Semantic-Based Retrieval Using Various Visual Features for Real-World Images

H. Y. Cui†, J. F. Cao†*, H. Shi‡, & E.C. Bacharoudis§

Department of Computer Science and Technology, Xinzhou Teachers University, Xinzhou 034000, China,
‡School of Computer Science and Technology, Taiyuan University of Science and Technology,
Taiyuan 030024, China
§Department of Mechanical Engineering, Instituto Superior Técnico, Technical University of Lisbon, Av. Rovisco Pais, 1049- 001 Lisboa, PortugaL

Cite this paper
H. Y. Cui†, J. F. Cao†*, H. Shi‡, & E.C. Bacharoudis§, “Semantic-Based Retrieval Using Various Visual Features for Real-World Images”, Journal of Mechanical Engineering Research and Developments, vol. 39, no. 2, pp. 324-339, 2016.  DOI: 10.7508/jmerd.2016.02.008

ABSTRACT:  Nowadays, the development of multimedia technology has resulted in the rapid growth of digital images and more and more digital images are available. It has become an urgent problem to efficiently retrieve images which can better meet the users’ requirements based on semantic. The description of semantic features for images becomes an issue in multimedia processing. In this paper, we propose novel methods which describe semantic of color, texture and shape features, and propose a method of image retrieval based on three features of color, texture and shape. Firstly, we extract low-level color feature using the method of sub- block, and then apply OCC model for describing the color semantic; Secondly, we use Canny operator to obtain the edge information of image in order to extract low-level texture feature of image, and then applied the improved Tamura model for describing the texture semantic; Thirdly, BP neural network is used to map from low-level features to high-level semantic features; Finally Zernike moment is used to extract shape features and image retrieval is implemented with three features. Choosing Corel image database as testing image database, experiments achieved good effect compared with the method only based on color semantic feature. Experimental results show that the proposed method is capable of meeting the users’ retrieval requirements and can lay a good foundation for solving “semantic gap” problem between low features and high semantic features.

Keywords : Semantic-Based Retrieval; Feature Extraction; Semantic mapping; BP Neural Network; Similarity.

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