Research on Feature Extraction Method of Geometric Image Recognition

The existing image recognition feature extraction technology cannot effectively transmit the image information if the extracted features are not complete, sufficient and inaccurate. It will have an impact on image processing, such as reducing the accuracy and efficiency of subsequent image recognition and image tracking. At the same time, the existing technology for image processing destroys the topological structure between image pixels, and the process is complicated in the calculation of high-dimensional data space. Therefore, this paper proposes a method and steps of feature extraction for collective image recognition, and makes relevant supplementary explanation and analysis.

[1]  Joshua D. Schwartz,et al.  Hierarchical Matching of Deformable Shapes , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[3]  Hui Wei,et al.  Multiscale Triangular Centroid Distance for Shape-Based Plant Leaf Recognition , 2016, ECAI.

[4]  I. Biederman,et al.  Surface versus edge-based determinants of visual recognition , 1988, Cognitive Psychology.

[5]  Yu Liu,et al.  Hierarchical projective invariant contexts for shape recognition , 2016, Pattern Recognit..

[6]  Wenyu Liu,et al.  Bag of contour fragments for robust shape classification , 2014, Pattern Recognit..

[7]  Naif Alajlan,et al.  Shape retrieval using triangle-area representation and dynamic space warping , 2007, Pattern Recognit..

[8]  Josef Kittler,et al.  Efficient and Robust Retrieval by Shape Content through Curvature Scale Space , 1998, Image Databases and Multi-Media Search.

[9]  Noel E. O'Connor,et al.  A multiscale representation method for nonrigid shapes with a single closed contour , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[10]  W. John Kress,et al.  Leafsnap: A Computer Vision System for Automatic Plant Species Identification , 2012, ECCV.

[11]  Haibin Ling,et al.  Shape Classification Using the Inner-Distance , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Wei Jia,et al.  Multiscale Distance Matrix for Fast Plant Leaf Recognition , 2012, IEEE Transactions on Image Processing.