Paper Info
Title
A smart content-based image retrieval system based on color and texture feature
Abstract
In this paper, three image features are proposed for image retrieval. In addition, a feature selection technique is also brought forward to select optimal features to not only maximize the detection rate but also simplify the computation of image retrieval. The first and second image features are based on color and texture features, respectively called color co-occurrence matrix (CCM) and difference between pixels of scan pattern (DBPSP) in this paper. The third image feature is based on color distribution, called color histogram for K-mean (CHKM). CCM is the conventional pattern co-occurrence matrix that calculates the probability of the occurrence of same pixel color between each pixel and its adjacent ones in each image, and this probability is considered as the attribute of the image. According to the sequence of motifs of scan patterns, DBPSP calculates the difference between pixels and converts it into the probability of occurrence on the entire image. Each pixel color in an image is then replaced by one color in the common color palette that is most similar to color so as to classify all pixels in image into k-cluster, called the CHKM feature. Difference in image properties and contents indicates that different features are contained. Some images have stronger color and texture features, while others are more sensitive to color and spatial features. Thus, this study integrates CCM, DBPSP, and CHKM to facilitate image retrieval. To enhance image detection rate and simplify computation of image retrieval, sequential forward selection is adopted for feature selection. Besides, based on the image retrieval system (CTCHIRS), a series of analyses and comparisons are performed in our experiment. Three image databases with different properties are used to carry out feature selection. Optimal features are selected from original features to enhance the detection rate.
Year
DOI
Venue
2009
10.1016/j.imavis.2008.07.004
Image Vision Comput.
Keywords
Field
DocType
feature selection,pixel color,image retrieval,image property,image databases,optimal feature,image detection rate,texture,color,motif,entire image,image feature,image retrieval system,co-occurrence,texture feature,smart content-based image retrieval,image features,k means,color histogram,co occurrence matrix,co occurrence
Computer vision,Pattern recognition,Feature detection (computer vision),Color histogram,Image texture,Feature (computer vision),Computer science,Binary image,Artificial intelligence,Content-based image retrieval,Visual Word,Color image
Journal
Volume
Issue
ISSN
27
6
Image and Vision Computing
Citations 
PageRank 
References 
98
2.34
20
Authors
3
Name
Order
Citations
PageRank
Chuen-Horng Lin123115.93
Rong-Tai Chen2982.68
Yung-Kuan Chan347633.67