Image classification using feature subset selection

Authors:
Sang-Sung Park;Kwang-Kyu Seo;Ho-Seok Moon;Young-Geun Shin;Dong-Sik Jang
Affiliations:
Industrial Systems and Information Engineering, Korea University, Sungbuk-Ku, Seoul, Korea;Industrial Information and Systems Engineering, Sangmyung University, Chungnam, Korea;Industrial Systems and Information Engineering, Korea University, Sungbuk-Ku, Seoul, Korea;Industrial Systems and Information Engineering, Korea University, Sungbuk-Ku, Seoul, Korea;Industrial Systems and Information Engineering, Korea University, Sungbuk-Ku, Seoul, Korea
Venue:
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
Year:
2006

Citing 9
Cited 0

Color indexing

International Journal of Computer Vision
VisualSEEk: a fully automated content-based image query system

MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying

IEEE Transactions on Pattern Analysis and Machine Intelligence
The TV-tree: an index structure for high-dimensional data

The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Query by Image and Video Content: The QBIC System

Computer
Similarity Indexing with the SS-tree

ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
The X-tree: An Index Structure for High-Dimensional Data

VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Selecting Bankruptcy Predictors Using a Support Vector Machine Approach

IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Support vector machines for spam categorization

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Classification technology is essential for fast retrieval in large database. This paper proposes a combining GA and SVM model to content-based image retrieval. The proposed method is also used to classification similar images from database. Joint HSV histogram and average entropy computed from gray-level co-occurrence matrices in the localized image region is employed as input vectors. Genetic algorithm is employed to select feature subsets eliminated irrelevant factors as used inputs and to determine the optimal parameters of Support Vector Machine. Experimental results show that the proposed model outperforms existing method.