1. Field of the Invention
This invention relates to a self-organizing pattern learning system. This invention particularly relates to a self-organizing pattern learning system for carrying out self-organizing learning operations on a plurality of optical patterns which are presented to the self-organizing pattern learning system.
2. Description of the Prior Art
Matching techniques have heretofore been used widely in the field of pattern recognition in image processing. One of the typical matching techniques is to accumulate image pattern models, which have been selected manually, as knowledge and to carry out discrimination of a target object by a matching operation. However, the matching technique has various drawbacks in that, for example, models of discrimination target objects are fixed, and therefore the technique cannot cope with a wide variety of changes in the target objects (such as changes in sizes, directions, and shapes of the target objects). Also, because ordering of image patterns is not effected, if the number of models of image patterns becomes large, it is difficult to ascertain whether or not a pattern necessary for discrimination of a target object is missing.
Recently, in order to solve the problems described above, a technique utilizing a neural network, which simulates the principle of information processing carried out by the brain of a human being, has been proposed. The technique utilizing a neural network, aims at carrying out the learning of models of image patterns by use of a learning model of the neural network and utilizing the results of the learning operation during the discrimination of a target object. Specifically, with the technique utilizing a neural network, an attempt is made to impart the flexible characteristics of the neural network to templates at the learning stage such that the templates can cope with a wide variety of changes in target objects.
By way of example, the learning models include Kohonen's self-organized mapping, which is described in, for example, "Self-Organization and Associative Memory" by T. Kohonen, Springer-Verlag, 1984. The Kohonen's self-organized mapping model learns topological mapping through self-organization. The topological mapping means that, for example, various signals which a human being has received from the outer world, i.e., various signals representing a certain group of patterns, are allocated to neurons on the cortex in accordance with a certain kind of rule reflecting the order of the patterns.
With the system in which the pattern learning technique utilizing Kohonen's self-organization is employed, instead of various pieces of information representing the patterns presented from the outer world being learned at random, such various pieces of information are classified in the system and learned (stored) in an arranged form in the neural network. In this manner, as many efficient pieces of information as possible can be stored in the system having a limited capacity. Therefore, recently, the system in which the pattern learning technique utilizing Kohonen's self-organization is employed has attracted particular attention.
Utilization of Kohonen's self-organization in rough classification pre-processing during character recognition has been reported in, for example, "Identification of JIS First and Second Level Printed Characters by Comb NET" by Toma, Iwata, et al., Nagoya Kogyo University, Autumn Collected Drafts of The Institute of Electronics and Communication Engineers of Japan, 1990.
Also, a technique for carrying out learning operations with Kohonen's self-organization as parallel processing by using optical devices, video devices, and a computer has been reported in, for example, "Self-Organizing Optical Neural Network for Unsupervised Learning" by Taiwei Lu, etc., Optical Engineering, Vol. 29, No. 9, pp. 1,107-1,113, 1990.
FIG. 37 shows an experimental system for self-organization carried out by Taiwei, et al.
As illustrated in FIG. 37, the experimental system is provided with an optical system. The optical system comprises a diffuser 114, a storing/displaying light valve layer 104, a multiple image forming lenses 105, an input light valve layer 109, an image forming lens 110, and correlation output detector arrays 111. The storing/displaying light valve layer 104 comprises polarizers 101 and 103. The storing/displaying light valve layer 104 also comprises k.times.1 number (in this example, 3.times.1 number) of storing/displaying light valves 102A, 102B, and 102C, which have i.times.j number of picture elements and are arrayed between the polarizers 101 and 103. The storing/displaying light valves 102A, 102B, and 102C display memory patterns mklij(t-1). The input light valve layer 109 comprises polarizers 106 and 107 and a light valve 108, which is located between the polarizers 106 and 107 and displays an input pattern xij(t) presented at the time t. The experimental system is also provided with a processing system having a computer 113. The computer 113 makes calculations for the memory patterns mklij(t), which is to be stored in the storing/displaying light valve layer 104 at the time t, from the information representing the values ykl(t), which are obtained from the correlation output detector arrays 111, and the input pattern xij(t).
First, the input pattern xij(t), which has been recorded by a camera 112, is presented through the computer 113 to the input light valve layer 109 and displayed thereon. Thereafter, reading light 115 passes through the diffuser 114 and is then irradiated to the storing/displaying light valve layer 104. The storing/displaying light valves 102A, 102B, and 102C of the storing/displaying light valve layer 104 store patterns, which have been presented before the time t, as memory patterns mklij(t-1) in accordance with a certain rule. Therefore, when the reading light 115 is irradiated to the storing/displaying light valve layer 104, each of the memory patterns mklij(t-1) stored in the storing/displaying light valves 102A, 102B, and 102C is read as an optical intensity pattern 116. An image of the optical intensity pattern 116, which has been radiated out of each of the storing/displaying light valves 102A, 102B, and 102C, is formed on the input light valve layer 109 by each of the multiple image forming lenses 105 and passes as light 117 through the input light valve layer 109. The light 117 passes through the image forming lens 110 and is detected by each of the correlation output detector arrays 111 as the degree of correlation between the input pattern and each of the memory patterns stored in the storing/displaying light valves 102A, 102B, and 102C.
In this case, the input pattern xij(t) is displayed on the input light valve layer 109. Therefore, the light 117 immediately after passing through the input light valve layer 109 carries each of the optical superposition patterns, which result from the superposition of the input pattern xij(t) upon the respective memory patterns mklij(t-1) displayed on the storing/displaying light valve layer 104. Specifically, the light 117 immediately after passing through the input light valve layer 109 carries each of k.times.1 number of optical patterns, which correspond to the results of product calculations represented by the formula EQU m.sub.klij (t-1).times.x.sub.ij (t) (1)
Also, the light 117 is condensed to each of the correlation output detector arrays 111. Therefore, by each of the correlation output detector arrays 111, the sum of the amounts of light carrying each optical pattern, which corresponds to the result of the calculation made with Formula (1), is detected as the information about brightness and darkness. Specifically, the result of the calculations made with the formula ##EQU1## is detected by each of the correlation output detector arrays 111. The result represents the degree of correlation ykl(t) between the input pattern xij(t) and each of the memory patterns mklij(t-1). Signals representing the degrees of correlation ykl(t), which have thus been detected, are fed into the computer 113.
The computer 113 weights patterns in accordance with the degrees of correlation ykl(t) and carries out the operations for updating the memory patterns stored in the storing/displaying light valve layer 104, i.e. the learning operations. For example, the character A is stored as the memory pattern in the storing/displaying light valve 102A of the storing/displaying light valve layer 104. Also, the characters B and C are respectively stored as the memory patterns in the storing/displaying light valves 102B and 102C. In such cases, if the input pattern xij(t) is A, the degree of correlation ykl(t) detected for the storing/displaying light valve 102A by the corresponding detector array of the correlation output detector arrays 111 will take a large value. Also, the degrees of correlation ykl(t) detected for the storing/displaying light valves 102B and 102C will take small values. Therefore, the pattern A is written with a new large weight in the storing/displaying light valve 102A. Also, the pattern A is not written or is written only with small weights in the storing/displaying light valves 102B and 102C. If the pattern presented to the input light valve layer 109 is B, the pattern B will be written with a large weight in the storing/displaying light valve 102B. Also, if the pattern presented to the input light valve layer 109 is C, the pattern C will be written with a large weight in the storing/displaying light valve 102C. In this manner, the operations for updating the memory patterns, i.e. the learning operations, are carried out.
The operations described above are iterated, and various patterns sequentially presented to the input light valve 109 are stored and learned in the storing/displaying light valve layer 104.
As described above, in the experimental system of Taiwei, et al., the self-organization of the patterns presented to the input light valve is effected in the storing/displaying light valve layer 104.
However, with the experimental system of Taiwei, et al., the optical operations between the storing/displaying light valves and the input light valve are carried out by using the multiple image forming lenses 105. Therefore, a certain length of the distance is required between the storing/displaying light valves and the input light valve. Accordingly, the scale of the apparatus for carrying out the self-organization cannot be kept small.
Also, with the experimental system of Taiwei, et al., the value of correlation between the output of each neuron (in the storing/displaying light valve layer) and the input pattern is detected optically in accordance with the Kohonen's self-organization. The operations for updating the memory patterns in accordance with the values of correlation are carried out by using the computer. Therefore, even if the calculations of the values of correlation are carried out in parallel and quickly by using the optical system, the entire processing for the self-organizing learning operations cannot be carried out quickly.
Further, with the experimental system of Taiwei, et al., 8.times.8 number of correlation degree detectors are used. Therefore, the number of the degrees of correlation detected in the experimental system of Taiwei, et al. is 64. Therefore, signals representing the detected degrees of correlation are fed from the correlation output detector arrays through 64 wires into the computer.
If the number of the correlation degree detectors is as small as 8.times.8, the number of the wires, through which the signals representing the degrees of correlation are fed into the computer, can be kept comparatively small. However, in cases where a larger number (e.g., 64.times.64 number) of correlation degree detectors are used and more complicated operation processing is to be carried out, a very large number (e.g. 64.times.64=4,096 number) of wires must be used. As a result, problems occur in that the size of the system for carrying out the self-organizing learning operations cannot be kept small, and in that the cost of the system cannot be kept low.