Improved PCNN Models And Their Applications In Invariant Texture Retrieval And Finding Shortest Path

Pulse Coupled Neural Network (PCNN) is an Artificial Neural Network which is proposed based on the mechanism of mammal visual cortical neurons. It is close to the approach of information processes by visual cortical neurons, and therefore is widely studied and used in the field of intelligent information processing. However, if the PCNN model could be improved and modified based on the requirements of practical application, the better results will be obtained.Since PCNN has so many useful characteristics, the applications of texture retrieval and finding shortest path by using PCNN have become some of the hottest research points. Many existing texture retrieval methods assume that all texture images are identical with respect to rotation, translation, scale and without distortion. However, it is hard to ensure that all the images have been captured under the conditions of the same angle, position, focal length and without distortion in the real world. Moreover, most existing invariant texture retrieval methods did not consider the influences of rotation, scale and translation at the same time, or the affine transform of the input texture. Although time series of PCNN have the invariant characteristics, it is valuable to improve PCNN model for obtaining more stable and effective feature vectors based on the practical application of texture retrieval. In addition, PCNN has the autowave and parallel processing characteristics, which makes it suitable for dealing with the shortest path problem. However, it will reduce the iteration times in solving the shortest path problem by doing some useful modifications in PCNN model based on the characteristics of shortest path problem.The paper focuses on the PCNN based invariant texture retrieval systems and shortest path problem. The main contents and innovative points of this dissertation are as follows:1. For invariant texture retrieval, a new DPCNN model (dual-output PCNN) is proposed. Compared with standard PCNN model, the proposed DPCNN has some new characteristics:(1) each neuron has two opportunities to get itself stimulated;(2) DPCNN model can automatically change the external stimulus of the neuron;(3) the received local stimulus from neighbors of a neuron is controlled by the external stimulus;(4) DPCNN model is believed to be coincident with human visual characteristic. The new characteristics are applied to obtain a more stable texture features in invariant texture retrieval. Experimental results demonstrate that DPCNN based texture retrieval method has better translation, scale, rotation and affine invariance. Moreover, once the texture images are contaminated by noise, DPCNN shows good performance as well.2. Color compensation is introduced in DPCNN model for its application in color texture retrieval. Because a single DPCNN model can be only used for one channel input stimulus, it can not be directly applied to the color texture retrieval. However, if the color texture images are processed only as gray texture images, it will lead to the insufficient utilization of color information. The method regards value channel as the regular input of DPCNN, and considers hue and saturation channels as its compensation inputs. Experimental results show that the proposed method outperforms the methods which only consider the gray texture and also outperforms the classical color texture retrieval method.3. The paper tried to propose a three channels color texture retrieval frame based on PCNN related models. The frame processes each color channel by using PCNN related models, and then combines the feature vectors of all channels under the condition that overall feature length will not increase. Experimental results show that the frame can effectively solve the PCNN related models based color texture retrieval problem.4. For shortest path problem, a new SAPCNN model (self-adaptive autowave PCNN) is proposed. The model can adjust the propagation speed of autowave adaptively according to the current network state, which will result in faster resolving time. In addition, SAPCNN model based finding shortest path method is applied to K shortest paths problem. Experimental results show that the method can reduce the time consumption for finding K shortest paths.