DocumentCode :
1562184
Title :
Contour-Based Image Retrieval
Author :
Zhang, Zhiyong ; Shi, Zhiping ; Shi, Zhiwei ; Shi, Zhongzhi
Author_Institution :
Chinese Acad. of Sci., Beijing
fYear :
2007
Firstpage :
522
Lastpage :
529
Abstract :
This paper proposes a strategy for retrieving multi-texture images based on contour and texture segmentation. Firstly, the contour of each texture primitive is extracted from an image and its Fourier descriptor is calculated. Thus, the contours of the texture primitives in the original image are clustered according to these shape descriptors. Then Gabor wavelet transform is applied to extract the features of texture primitives for each group, so the image can be represented by a set of feature vectors in feature space. Finally, an improved and noise insensitive Hausdorff distance is used to calculate the distance between two feature vector sets. Furthermore, the retrieval of multi-texture images can be implemented. A large amount of experiments prove that our method has higher retrieval precision, compared with the state-of-arts methods.
Keywords :
Fourier transforms; edge detection; feature extraction; image retrieval; image segmentation; image texture; wavelet transforms; Fourier descriptor; Gabor wavelet transform; Hausdorff distance; contour-based image retrieval; feature extraction; image texture segmentation; multitexture image retrieval; texture primitive; Data mining; Feature extraction; Foot; Fourier transforms; Gabor filters; Image retrieval; Image segmentation; Multi-stage noise shaping; Shape; Wavelet transforms; Fourier transform; Gabor wavelet; Hausdorff distance; contour; multiple texture;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cognitive Informatics, 6th IEEE International Conference on
Conference_Location :
Lake Tahoo, CA
Print_ISBN :
9781-4244-1327-0
Electronic_ISBN :
978-1-4244-1328-7
Type :
conf
DOI :
10.1109/COGINF.2007.4341932
Filename :
4341932
Link To Document :
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