A Feature Extraction Method Combining Color-Shape for Binocular Stereo Vision Image
Fengfeng Duan
Journal of Multimedia Processing and Technologies, Vol. 9, No. 2, 2018.
DOI: https://doi.org/10.6025/jmpt/2018/9/2/45-58
Link: http://www.dline.info/jmpt/fulltext/v9n2/jmptv9n2_2.pdf
Abstract:
Feature extraction is the key and foundation of content-based retrieval of video and image. In order to realize
the content-based index and retrieval of binocular stereo vision resources efficiently, the method of feature extraction based
on Principal Component Analysis-Histogram of Oriented Depth Gradient (PCA-HODG) and Main Color Histograms (MCH)
is proposed. In the method, on the one hand, for the depth map obtained from matching of right image and left image, the PCAHODG
algorithm is proposed to extract shape features. In the algorithm, edge detection and gradient calculation in depth
map windows are performed to obtain the regional shape histogram features. Moreover, sliding window detection over a
depth map is performed to extract the full features. At the same time, in feature extraction of depth map windows and full depth
map, principal component analysis is used to realize dimensional reduction respectively. On the other hand, for the left image
of binocular stereo vision, the improved MCH algorithm is used to extract color features. Then the shape and color descriptors
can be obtained as 2-dimensional factors for similarity calculation. The experimental results show that the proposed
method can detect and extract the features of binocular stereo vision image more effectively and achieve similar classification
more accurately compared with the existing HOD, RSDF and GIF algorithms. Moreover, the proposed method also has better
robustness.