Using Hilbert scan on statistical color space partitioning

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.

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► The BIC features inherit the problem that the utilization rates are not balanced.
► A One-pass Partitioning Method (OPM) is efficient in partitioning the color space.
► The proposed OPM can balance the utilization rates of each feature.