A Feature Extraction Method Combining Color-Shape for Binocular Stereo Vision Image

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.