Survey of Image Recognition Algorithms

Images are the basis of human vision and an important way for humans to communicate with the world. Making computers with efficient and accurate image recognition is an important technical field of artificial intelligence. In recent years, image recognition technology has developed rapidly, and many new recognition technologies have emerged. This article first outlines the development process of image recognition technology; expounds the entire technical process of image recognition; introduces the main techniques of image preprocessing and image segmentation; and then summarizes the extraction of different features of images and common image classification algorithms. Such as: KNN, SVM, BP neural network, CNN. Finally, the future research development trend of image recognition is analyzed and prospected.

[1]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[2]  Tony Lindeberg,et al.  Edge Detection and Ridge Detection with Automatic Scale Selection , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Jiangxin Zhang,et al.  一种基于GMP-LeNet网络的车牌识别方法 (License Plate Recognition Method Based on GMP-LeNet Network) , 2018, 计算机科学.

[4]  Pengcheng Yan,et al.  [Application of the Identification of Mine Water Inrush with LIF Spectrometry and KNN Algorithm Combined with PCA]. , 2016, Guang pu xue yu guang pu fen xi = Guang pu.

[5]  Ramin Zabih,et al.  Histogram refinement for content-based image retrieval , 1996, Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96.

[6]  Xiao Hui-hu.  Improved the KNN Algorithm Based on Related to the Distance of Attribute Value , 2013 .

[7]  J. M. Zachary,et al.  An Information Theoretic Approach to Content Based Image Retrieval. , 2000 .

[8]  Shih-Fu Chang,et al.  Transform features for texture classification and discrimination in large image databases , 1994, Proceedings of 1st International Conference on Image Processing.

[9]  David B. Cooper,et al.  Simple Parallel Hierarchical and Relaxation Algorithms for Segmenting Noncausal Markovian Random Fields , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[11]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[12]  John K. Tsotsos,et al.  50 Years of object recognition: Directions forward , 2013, Comput. Vis. Image Underst..

[13]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  Norio Baba,et al.  A new approach for finding the global minimum of error function of neural networks , 1989, Neural Networks.

[15]  Markus A. Stricker,et al.  Similarity of color images , 1995, Electronic Imaging.

[16]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[17]  Vladimir Naumovich Vapni.  The Nature of Statistical Learning Theory , 1995 .

[18]  Wang Kai-jue.  Improve KNN algorithm based on entropy method , 2009 .

[19]  C. Arun Priya,et al.  An efficient leaf recognition algorithm for plant classification using support vector machine , 2012, International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012).

[20]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[21]  Hans P. Moravec.  Towards Automatic Visual Obstacle Avoidance , 1977, IJCAI.