Research On The Biologically Inspired Image Categorization Algorithms

Object recognition has been a popular area of intense research, and is also a very challenging task in computer vision, while human vision with unique processing me-chanism has the ability to recognize objects rapidly, accurately, and effortlessly. Al-though the performance of the object recognition has been improved with the vision algorithms, which have been proposed during the last decades, none of these algo-rithms available today can surpass the performance of the human brain. Thus, build-ing an artificial system that emulates object recognition in cortex or matches with human vision as closely as possible has always been an attractive but elusive goal. To design and build robust object recognition would be beneficial for many fields and applications, such as security surveillance, robot navigation, clinical image under-standing, etc.This paper mainly focuses on object recognition and image categorization algo-rithms. In accordance with the current achievements of neurobiology and neurophysi-ology, and considering the biological learning mechanism, we combine the Hierar-chical Model and X (HMAX) model and the extreme learning machine (ELM), and then construct a novel biologically inspired feed forward network for the image cate-gorization. The main work and contributions of this paper are summarized as follows:Firstly, the research of object recognition in computer vision and biological vision, and the problem and strategy of object recognition and image categorization. Object recognition includes two cases, identification and categorization, respectively. Its strategy and solution has been discussed in the view of physiology and computation. There are two main steps in object recognition based on image. One is to generate an effective feature representation of an image that includes sufficient information, and the other is to classify the feature representation with a proper classifier. This provides general theoretical supports for the design of object recognition and image categoriza-tion algorithms.Secondly, the design and implementation of the biologically inspired image ca-tegorization algorithm network. The proposed image categorization algorithm network consists of five layers, namely, S1 layer, C1 layer, S2 layer, C2 layer and H layer, that is, S1-C1-S2-C2-H. The previous four layers focus on the design of feature represen-tation structure, and build high-level image feature representations based on physio-logical data about the mammalian visual pathways. The H layer at last pays attention on biological learning mechanism of the human brain, which is implemented with ELM, and acts the role of classifying the image feature representations. Meanwhile, the proposed network tries to combine the biological feature building mechanism and the biological learning mechanism together, which provides a new train of thought for in-depth study on object recognition.Thirdly, the experimental results and analysis for the biologically inspired image categorization algorithm network. In this paper, four groups of experiments on the three images datasets have been performed, which are predicting accuracy verifica-tion, parameters sensitivity analysis, confusion tables calculation, and hidden nodes comparison, respectively. Experimental results show that the proposed network has good performance with fast learning speed. And it shows a potential application prospect in the engineering.Conclusively, the proposed biologically inspired image categorization algorithm network, seems a little step towards human brain alike recognizing and learning, and is also an attempt to bridge the gap between computer vision and neuroscience.