8 March 2018 Modeling of biologically motivated self-learning equivalent-convolutional recurrent-multilayer neural structures (BLM_SL_EC_RMNS) for image fragments clustering and recognition
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Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 106091D (2018) https://doi.org/10.1117/12.2285797
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
Abstract
The biologically-motivated self-learning equivalence-convolutional recurrent-multilayer neural structures (BLM_SL_EC_RMNS) for fragments images clustering and recognition will be discussed. We shall consider these neural structures and their spatial-invariant equivalental models (SIEMs) based on proposed equivalent two-dimensional functions of image similarity and the corresponding matrix-matrix (or tensor) procedures using as basic operations of continuous logic and nonlinear processing. These SIEMs can simply describe the signals processing during the all training and recognition stages and they are suitable for unipolar-coding multilevel signals. The clustering efficiency in such models and their implementation depends on the discriminant properties of neural elements of hidden layers. Therefore, the main models and architecture parameters and characteristics depends on the applied types of non-linear processing and function used for image comparison or for adaptive-equivalent weighing of input patterns. We show that these SL_EC_RMNSs have several advantages, such as the self-study and self-identification of features and signs of the similarity of fragments, ability to clustering and recognize of image fragments with best efficiency and strong mutual correlation. The proposed combined with learning-recognition clustering method of fragments with regard to their structural features is suitable not only for binary, but also color images and combines self-learning and the formation of weight clustered matrix-patterns. Its model is constructed and designed on the basis of recursively continuous logic and nonlinear processing algorithms and to k-average method or method the winner takes all (WTA). The experimental results confirmed that fragments with a large numbers of elements may be clustered. For the first time the possibility of generalization of these models for space invariant case is shown. The experiment for an images of different dimensions (a reference array) and fragments with diferent dimensions for clustering is carried out. The experiments, using the software environment Mathcad showed that the proposed method is universal, has a significant convergence, the small number of iterations is easily, displayed on the matrix structure, and confirmed its prospects. Thus, to understand the mechanisms of self-learning equivalence-convolutional clustering, accompanying her to the competitive processes in neurons, and the neural auto-encoding-decoding and recognition principles with the use of self-learning cluster patterns is very important which used the algorithm and the principles of non-linear processing of two-dimensional spatial functions of images comparison. The experimental results show that such models can be successfully used for auto- and hetero-associative recognition. Also they can be used to explain some mechanisms, known as "the reinforcementinhibition concept". Also we demonstrate a real model experiments, which confirm that the nonlinear processing by equivalent function allow to determine the neuron-winners and customize the weight matrix. At the end of the report, we will show how to use the obtained results and to propose new more efficient hardware architecture of SL_EC_RMNS based on matrix-tensor multipliers. Also we estimate the parameters and performance of such architectures.
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Vladimir G. Krasilenko, Alexander A. Lazarev, Diana V. Nikitovich, "Modeling of biologically motivated self-learning equivalent-convolutional recurrent-multilayer neural structures (BLM_SL_EC_RMNS) for image fragments clustering and recognition", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106091D (8 March 2018); doi: 10.1117/12.2285797; https://doi.org/10.1117/12.2285797
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