Paper Info
Title
Searching Images in a Textile Image Database.
Abstract
In this paper, a textile image search system is proposed to query similar textile images in an image database. Five feature descriptors about the color, texture, and shape defined in the MPEG-7 specification, which are relevant to textile image characteristics, are extracted from a dataset. First, we tune the feature weights using a genetic algorithm, based on a predefined training dataset. Then, for each extracted feature descriptor, we use K-means to partition it into four clusters and combine them together to obtain an MPEG-7 signature. Finally, when users input a query image, the system finds out similar images by combining the results based on MPEG-7 signatures and the ones in three nearest classes. The experimental results show that the similar images returned from an image database to a query textile image are acceptable for humans and with good quality.
Year
DOI
Venue
2014
10.1007/978-3-319-11897-0_32
ADVANCES IN SWARM INTELLIGENCE, ICSI 2014, PT II
Keywords
Field
DocType
CBIR,genetic algorithm,K-means,MPEG-7 specification,weight tuning
Computer vision,k-means clustering,Feature descriptor,Feature detection (computer vision),Pattern recognition,Computer science,Artificial intelligence,Image database,Genetic algorithm
Conference
Volume
ISSN
Citations 
8795
0302-9743
0
PageRank 
References 
Authors
0.34
6
2
Name
Order
Citations
PageRank
Yin-Fu Huang151.13
Sheng-Min Lin200.34