Bidirectional Self-organizing Neural Network Research Articles

A quantum bi-directional self-organizing neural network (QBDSONN) architecture for binary object extraction from a noisy perspective

This article proposes an efficient technique for binary object extraction in real time from noisy background using quantum bi-directional self-organizing neural network (QBDSONN) architecture. QBDSONN exploits the power of quantum computation. It is composed of three second order neighborhood topology based inter-connected layers of neurons (represented by qubits) arranged as input, intermediate and output layers. In the suggested network architecture, the inter-connection weights and activation values are represented by rotation gates. A self-supervised learning algorithm, suggested in this proposed architecture, relies on the steepest descent algorithm. The quantum neurons enjoy full-connectivity in each layer of the network architecture. The image pixels in terms of qubits are self-organized in between the intermediate or hidden and output layers of the QBDSONN architecture using counter-propagation of the quantum states to obviate time consuming quantum back propagation algorithm. In the final phase, quantum measurement is carried out at the output layer to eliminate superposition of the quantum states of the outputs. In order to establish the result, the proposed QBDSONN architecture is applied on an artificial synthetic and on a real life spanner image with different degrees of uniform and Gaussian noises. Experimental results show that QBDSONN outperforms both its classical counterpart and the supervised auto-associative Hopfield network as far as extraction time is concerned and it retains the shapes of the extracted images with great precision. Experiments are also carried out using a linear method named local statistics (Wiener filter) and a nonlinear technique named median filter with adaptive discrete wavelet transformations (DWT) for binary object extraction to show the dominance of the proposed QBDSONN with respect to the quality of extracted images. Finally, a statistical significance of the proposed QBDSONN is reported by applying 2 sample one sided Kolmogorov–Smirnov test with the existing methods.

A Comparative Study on the Development of Binary Object Extraction System using Different Self Organizing Neural Network

Accurately Extraction of a binary object from a noisy perspective has been a daunting task in the field of pattern recognition. Several techniques have been tried to optimally solve the problem of denoising of object over the decades. In this paper, different binary object extraction methods are reviewed which are basically guided by different SelfOrganizing Neural Networks (SONN) architectures as BiDirectional Self Organizing Neural Network (BDSONN), multi-Layer Self Organizing neural Network (MLSONN) and quantum version of MLSONN (QMLSONN). The result shows that QMLSONN outperforms over other network architectures in terms of time and also it restores shape of the object with great accuracy.

A parallel bi-directional self-organizing neural network (PBDSONN) architecture for color image extraction and segmentation

The parallel self-organizing neural network (PSONN) architecture uses bilevel sigmoidal activation functions for the purpose of extraction of embedded objects from pure color noisy perspectives. The process of extraction often involves an enhancement of the images under consideration. The network employs multilevel sigmoidal activation function to segment true color images. Both these activation functions are characterized by fixed thresholding parameters, which do not incorporate the underlying heterogeneity in the image intensity gamut. Methods for incorporating dynamic thresholding mechanisms into the thresholding characteristics of the PSONN architecture are investigated in this paper.We also propose a parallel bi-directional self-organizing neural network (PBDSONN) architecture to address the limitations of the PSONN architecture. Three constituent BDSONNs in the proposed architecture process color component information by embedded adaptive fuzzy context sensitive thresholding (CONSENT) mechanisms. A source layer feeds the BDSONNs with input color component information. Another sink layer fuses the processed color component information into resultant outputs.Comparative results of the quality of the extracted/segmented images indicate the efficacy of the proposed PBDSONN architecture over the PSONN architecture with fixed as well as dynamic thresholding mechanisms.

Multilevel image segmentation with adaptive image context based thresholding

Neural network based image segmentation techniques primarily focus on the selection of appropriate thresholding points in the image feature space. Research initiatives in this direction aim at addressing this problem of effective threshold selection for activation functions. Multilevel activation functions resort to fixed and uniform thresholding mechanisms. These functions assume homogeneity of the image information content. In this paper, we propose a collection of adaptive thresholding approaches to multilevel activation functions. The proposed thresholding mechanisms incorporate the image context information in the thresholding process. Applications of these mechanisms are demonstrated on the segmentation of real life multilevel intensity images using a self-supervised multilayer self-organizing neural network (MLSONN) and a supervised pyramidal neural network (PyraNet). We also present a bi-directional self-organizing neural network (BDSONN) architecture suitable for multilevel image segmentation. The architecture uses an embedded adaptive thresholding mechanism to a characteristic multilevel activation function. The segmentation efficiencies of the thresholding mechanisms evaluated using four unsupervised measures of merit, are reported for the three neural network architectures considered.

A Self Supervised Bi-directional Neural Network (BDSONN) Architecture for Object Extraction Guided by Beta Activation Function and Adaptive Fuzzy Context Sensitive Thresholding

A multilayer self organizing neural neural network (MLSONN) architecture for binary object extraction, guided by a beta activation function and characterized by backpropagation of errors estimated from the linear indices of fuzziness of the network output states, is discussed. Since the MLSONN architecture is designed to operate in a single point fixed/uniform thresholding scenario, it does not take into cognizance the heterogeneity of image information in the extraction process. The performance of the MLSONN architecture with representative values of the threshold parameters of the beta activation function employed is also studied. A three layer bidirectional self organizing neural network (BDSONN) architecture comprising fully connected neurons, for the extraction of objects from a noisy background and capable of incorporating the underlying image context heterogeneity through variable and adaptive thresholding, is proposed in this article. The input layer of the network architecture represents the fuzzy membership information of the image scene to be extracted. The second layer (the intermediate layer) and the final layer (the output layer) of the network architecture deal with the self supervised object extraction task by bi-directional propagation of the network states. Each layer except the output layer is connected to the next layer following a neighborhood based topology. The output layer neurons are in turn, connected to the intermediate layer following similar topology, thus forming a counter-propagating architecture with the intermediate layer. The novelty of the proposed architecture is that the assignment/updating of the inter-layer connection weights are done using the relative fuzzy membership values at the constituent neurons in the different network layers. Another interesting feature of the network lies in the fact that the processing capabilities of the intermediate and the output layer neurons are guided by a beta activation function, which uses image context sensitive adaptive thresholding arising out of the fuzzy cardinality estimates of the different network neighborhood fuzzy subsets, rather than resorting to fixed and single point thresholding. An application of the proposed architecture for object extraction is demonstrated using a synthetic and a real life image. The extraction efficiency of the proposed network architecture is evaluated by a proposed system transfer index characteristic of the network. Keywords— Beta activation function, fuzzy cardinality, multilayer self organizing neural network, object extraction, Manuscript received October 28, 2006. Siddhartha Bhattacharyya is with the Department of Computer Science and Information Technology, University Institute of Technology, The University of Burdwan, Burdwan-713 104, India, phone: +91-342-2558776; fax: +91342-2558776; email: siddhartha.bhattacharyya@gmail.com. Paramartha Dutta is with the Department of Computer Science and Engineering, Kalyani Government Engineering College, Kalyani, Nadia-741 235, India, email: paramartha.dutta@gmail.com. Ujjwal Maulik is with the Department of Computer Science and Engineering, Jadavpur University, Kolkata, India, email: drumaulik@cse.jdvu.ac.in. Prashanta Kumar Nandi is with the Department of Computer Science and Technology, Bengal Engineering & Science University, Shibpore, Howrah, India, email: nandipkn@rediffmail.com.

Binary object extraction using bi-directional self-organizing neural network (BDSONN) architecture with fuzzy context sensitive thresholding

A novel neural network architecture suitable for image processing applications and comprising three interconnected fuzzy layers of neurons and devoid of any back-propagation algorithm for weight adjustment is proposed in this article. The fuzzy layers of neurons represent the fuzzy membership information of the image scene to be processed. One of the fuzzy layers of neurons acts as an input layer of the network. The two remaining layers viz. the intermediate layer and the output layer are counter-propagating fuzzy layers of neurons. These layers are meant for processing the input image information available from the input layer. The constituent neurons within each layer of the network architecture are fully connected to each other. The intermediate layer neurons are also connected to the corresponding neurons and to a set of neighbors in the input layer. The neurons at the intermediate layer and the output layer are also connected to each other and to the respective neighbors of the corresponding other layer following a neighborhood based connectivity. The proposed architecture uses fuzzy membership based weight assignment and subsequent updating procedure. Some fuzzy cardinality based image context sensitive information are used for deciding the thresholding capabilities of the network. The network self organizes the input image information by counter-propagation of the fuzzy network states between the intermediate and the output layers of the network. The attainment of stability of the fuzzy neighborhood hostility measures at the output layer of the network or the corresponding fuzzy entropy measures determine the convergence of the network operation. An application of the proposed architecture for the extraction of binary objects from various degrees of noisy backgrounds is demonstrated using a synthetic and a real life image.