The goal of this work is to learn and retrieve a sequence of highly correlated patterns using a Hopfield-type of attractor neural network (ANN) with a small-world connectivity distribution. For this model, we propose ...
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The goal of this work is to learn and retrieve a sequence of highly correlated patterns using a Hopfield-type of attractor neural network (ANN) with a small-world connectivity distribution. For this model, we propose a weight learning heuristic which combines the pseudo-inverse approach with a row-shifting schema. The influence of the ratio of random connectivity on retrieval quality and learning time has been studied. Our approach has been successfully tested on a complex pattern, as it is the case of traffic video sequences, for different combinations of the involved parameters. Moreover, it has demonstrated to be robust with respect to highly variable frame activity. (C) 2011 Elsevier B.V. All rights reserved.
We propose a computational model of recognition of the cerebral cortex, based on an approximate belief revision algorithm. The algorithm calculates the MPE (most probable explanation) of Bayesian networks with a linea...
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ISBN:
(纸本)9781424496365
We propose a computational model of recognition of the cerebral cortex, based on an approximate belief revision algorithm. The algorithm calculates the MPE (most probable explanation) of Bayesian networks with a linear-sum CPT (conditional probability table) model. Although the proposed algorithm is simple enough to be implemented by a fixed circuit, results of the performance evaluation show that this algorithm does not have bad approximation accuracy. The mean convergence time is not sensitive to the number of nodes if the depth the network is constant. The computation amount is linear to the number of nodes if the number of edges per node is constant. The proposed algorithm can be used as a part of a learning algorithm for a kind of sparse-coding, which reproduces orientation selectivity of the primary visual area. The circuit that executes the algorithm shows better correspondence to the anatomical structure of the cerebral cortex, namely its six-layer and columnar features, than the approximate belief propagation algorithm that has been proposed before. These results suggest that the proposed algorithm is a promising starting point for the model of the recognition mechanism of the cerebral cortex.
Example-based image super-resolution techniques model the co-occurrence patterns between the middle and high frequency layers of example images to estimate the missing high frequency component for low resolution input...
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Example-based image super-resolution techniques model the co-occurrence patterns between the middle and high frequency layers of example images to estimate the missing high frequency component for low resolution input. However, many existing approaches seek to estimate the optimal solution within a small set of candidates by using empirical criteria. Hence their representational performance is limited by the quality of the candidate set, and the generated super-resolution image is unstable, with noticeable artifacts. In this paper, we propose a novel image super-resolution method based on learning the sparse association between input image patches and the example image patches. We improve an existing sparse-coding algorithm to find sparse association between image patches. We also propose an iterative training strategy to learn a redundancy reduced basis set to speed up the super-resolution process. Comparing to existing example-based approaches, the proposed method significantly improves image quality, and the produced super-resolution images are sharp and natural, with no obvious artifact. (C) 2009 Elsevier B.V. All rights reserved.
Neural networks in the visual system may be performing sparsecoding of learnt local features that are qualitatively very similar to the receptive fields of simple cells in the primary visual cortex, V1. In convention...
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Neural networks in the visual system may be performing sparsecoding of learnt local features that are qualitatively very similar to the receptive fields of simple cells in the primary visual cortex, V1. In conventional sparsecoding, the data are described as a combination of elementary features involving both additive and subtractive components. However, the fact that features can 'cancel each other out' using subtraction is contrary to the intuitive notion of combining parts to form a whole. Thus, it has recently been argued forcefully for completely non-negative representations. This paper presents Non-Negative sparsecoding (NNSC) applied to the learning of facial features for face recognition and a comparison is made with the other part-based techniques, Non-negative Matrix Factorization (NMF) and Local-Non-negative Matrix Factorization (LNMF). The NNSC approach has been tested on the Aleix-Robert (AR), the Face Recognition Technology (FERET), the Yale B, and the Cambridge ORL databases, respectively. In doing so, we have compared and evaluated the proposed NNSC face recognition technique under varying expressions, varying illumination, occlusion with sunglasses, occlusion with scarf, and varying pose. Tests were performed with different distance metrics such as the L-1-metric, L-2-metric, and Normalized Cross-Correlation (NCC). All these experiments involved a large range of basis dimensions. In general, NNSC was found to be the best approach of the three part-based methods, although it must be observed that the best distance measure was not consistent for the different experiments.
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