직선요소와 휘도영역 기반 복합 정지영상 인식자
Compound Image Identifier Based on Linear Component and Luminance Area
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초록
As personal or compact devices with image acquisition functionality are becoming easily available for common users, the voluminous images that need to be managed by image related services or systems demand efficient and effective methods in the perspective of image identification. The objective of image identification is to associate an image with a unique identifier. Moreover, whenever an image identifier needs to be regenerated, the newly generated identifier should be consistent. In this paper, we propose three image identifier generation methods utilizing image features: linear component, luminance area, and combination of both features. The linear component based method exploits the information of distribution of partial lines over an image, while the luminance area based method utilizes the partition of an image into a number of small areas according to the same luminance degree. The third method is proposed in order to take advantage of both former methods. In this paper, we also demonstrate the experimental evaluations for uniqueness and similarity analysis that have shown favorable results.
Abstract
As personal or compact devices with image acquisition functionality are becoming easily available for common users, the voluminous images that need to be managed by image related services or systems demand efficient and effective methods in the perspective of image identification. The objective of image identification is to associate an image with a unique identifier. Moreover, whenever an image identifier needs to be regenerated, the newly generated identifier should be consistent. In this paper, we propose three image identifier generation methods utilizing image features: linear component, luminance area, and combination of both features. The linear component based method exploits the information of distribution of partial lines over an image, while the luminance area based method utilizes the partition of an image into a number of small areas according to the same luminance degree. The third method is proposed in order to take advantage of both former methods. In this paper, we also demonstrate the experimental evaluations for uniqueness and similarity analysis that have shown favorable results.
참고문헌 (11)
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