Paper Title:
Web Image Classification Using an Optimized Feature Set
  Abstract

Redundant images currently abundant in World Wide Web pages need to be removed in order to transform or simplify the Web pages for suitable display in small-screened devices. Classifying removable images on the Web pages according to their uniqueness of content will allow simpler representation of Web pages. For such classification, machine learning based methods can be used to categorize images into two groups; eliminable and non-eliminable. We use two representative learning methods, the Naïve Bayesian classifier and C4.5 decision trees. For our Web image classification, we propose new features that have expressive power for Web images to be classified. We apply image samples to the two classifiers and analyze the results. In addition, we propose an algorithm to construct an optimized subset from a whole feature set, which includes most influential features for the purposes of classification. By using the optimized feature set, the accuracy of classification is found to improve markedly.

  Info
Periodical
Key Engineering Materials (Volumes 277-279)
Edited by
Kwang Hwa Chung, Yong Hyeon Shin, Sue-Nie Park, Hyun Sook Cho, Soon-Ae Yoo, Byung Joo Min, Hyo-Suk Lim and Kyung Hwa Yoo
Pages
361-368
DOI
10.4028/www.scientific.net/KEM.277-279.361
Citation
S. S. Cho, D. W. Han, C. J. Hwang, "Web Image Classification Using an Optimized Feature Set", Key Engineering Materials, Vols. 277-279, pp. 361-368, 2005
Online since
January 2005
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: James J. Hensman, Rob J. Barthorpe
Abstract:The optimal selection of discriminatory features from large datasets remains a pressing problem in damage identification. In this paper, a...
151
Authors: Li Min Wang, Xiong Fei Li, Xue Cheng Wang
Abstract:Dimensionality reduction is useful for improving the performance of Bayesian networks. In this paper we suggest an effective method of...
240
Authors: Dong Wang, Shi Huan Xiong
Chapter 8: Nanomaterials and Nanomanufacturing
Abstract:The learning sequence is an important factor of affecting the study effect about incremental Bayesian classifier. Reasonable learning...
1455
Authors: Xiao Dan Zhu, Jin Song Su, Qing Feng Wu, Huai Lin Dong
Chapter 1: Mechatronics
Abstract:Naive Bayes classification algorithm is an effective simple classification algorithm. Most researches in traditional Naive Bayes...
460
Authors: Yan Feng Zhang, Ting Ting Li
Chapter 3: Data Acquisition and Data Processing, Computational Techniques
Abstract:C4.5, Bayesian network and Sequential Minimal Optimization (SMO) are three typical classification algorithms in data mining. Using...
963