Image feature extraction method for person re-identification

An image feature extraction method for person re-identification, comprising:

• performing person re-identification by means of aligned local descriptor extraction and graded global feature extraction;
• performing the aligned local descriptor extraction by processing an image by affine transformation and performing a summation pooling operation on image block features of adjacent regions to obtain an aligned local descriptor;
• reserving spatial information between inner blocks of the image for the aligned local descriptor;
• and
• performing the graded global feature extraction by grading a positioned pedestrian region block and solving a corresponding feature mean value to obtain a global feature;
• the method further comprising:
• 1) pre-processing the image to eliminate the effect of illumination on the image;
• 2) extracting the aligned local descriptor, comprising:
• 21) performing affine transformation on the image to obtain multiple images;
• 22) performing image block segmentation on all images generated after affine transformation, extracting features, and generating corresponding feature vector maps;
• 23) superimposing all the feature vector maps in space, performing a summation pooling operation on the image block features of the adjacent positions, and obtaining corresponding local descriptors;
• and
• 24) connecting all local descriptors in the image in order to obtain an aligned local descriptor;
• 3) extracting graded global feature, comprising:
• 31) using the foreground extraction method to perform significance detection on the image to obtain a corresponding significance image;
• 32) enhancing the contrast of significance image obtained;
• 33) locating image blocks of the pedestrian area;
• 34) classifying the image blocks of the pedestrian area to obtain multi-level image blocks of pedestrian area;
• 35) performing an average pooling operation on each level of the image blocks of the pedestrian area to obtain the feature of the level;
• and
• 36) connecting features of multi levels and obtaining graded global feature;
• and
• 4) calculating similarity of the images according to the aligned local descriptor extracted in Step 2) and the graded global feature extracted in Step 3) by using the metric learning method, thereby performing person re-identification.