Using Hilbert scan on statistical color space partitioning

Authors:
Yi-Leh Wu;Cheng-Yuan Tang;Yuan-Ming Yeh;Wei-Chih Hung
Affiliations:
Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan;Department of Information Management, Huafan University, Taipei, Taiwan;Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan;Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
Venue:
Computers and Electrical Engineering
Year:
2013

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Abstract

This study proposes a method to combine the k-Nearest Neighbor (k-NN) algorithm and the Support Vector Machine (SVM) method to increase the image annotation accuracy. Image annotation is widely employed in domains such as web image classification, search, military, and biomedicine. Although the traditional Border/Interior pixel Classification (BIC) features are very efficient and compact when applied to image annotation to capture color, shape, and texture information, the color space histogram utilization rates are not balanced. The experiment results show that the Hilbert-scan method and the One-pass Partitioning Method (OPM) can effectively overcome the imbalance problem.