Retrainable Neural Networks For Image Analysis And

A novel approach is presented in this paper for improving the performance of neural network classifiers in image recognition, segmentation or coding applications, based on a retraining procedure at the user level. The procedure includes a maximum a posteriori (MAP) estimation technique for optimally selecting a retraining data set from the image applied to the network during real life operation, a decision mechanism for automatic activation of network retraining and a neural network module which performs the classification task. The extracted feature set, used for retraining the network, can include additional elements compared to those used in the network initial training phase, so that it better fits the specific application data under consideration. Results are presented which illustrate the theoretical developments as well as the performance of the proposed approach in real life experiments 1

Statistical Multiplexing and Quality of Service Control of VBR MPEG Video

In this paper efficient modeling of VBR MPEG coded video sources is proposed by appropriately combining properties of frame and GOP layer signals. In particular, a Markov chain is presented for modeling the video activity, the states of which correspond to correlated AR models responsible for generating the I, P and B frames. Furthermore, an adaptive implementation of the AR coefficients is accomplished in cases that we are interested in video traffic prediction. Experimental results using long duration sequences are provided to indicate the good performance of the proposed modeling scheme

A Neural Network-Genetic Algorithm Scheme for Optimal Grouping of Individual Cores in Three-Phase Distributed Transformers

This paper presents an effective method to reduce the iron losses of wound core distribution transformers based on a combined neural network-genetic algorithm approach. The originality of the work presented in this paper is that it tackles the iron loss reduction problem during the transformer production phase, while previous works were concentrated on the design phase. More specifically, neural networks effectively use measurements taken at the first stages of core construction in order to predict the iron losses of the assembled transformers, while genetic algorithms are used to improve the grouping process of the individual cores by reducing iron losses of assembled transformers. The proposed method has been tested on a real transformer manufacturing industry and has resulted in a significant cost reduction

Stochastic Search Algorithms for Optimal Content-Based Sampling Of Video Sequences

A video content representation framework is proposed in this paper, for extracting limited, but meaningful, information of video data, directly from the MPEG compressed domain. In particular, extraction of several representative shots is performed for each video sequence in a content based rate sampling framework. An approach, based on minimization of a cross-correlation criterion of the video frames has been adopted for the shot selection. For efficient implementation of the latter approach, a logarithmic search in a stochastic framework is proposed. The method always converges to the global minimum as is proven in the paper

An Adaptable Neural-Network Model for Recursive Nonlinear Traffic Prediction and Modeling of MPEG Video Sources

Multimedia services and especially digital video is expected to be the major traffic component transmitted over communication networks [such as internet protocol (IP)-based networks]. For this reason, traffic characterization and modeling of such services are required for an efficient network operation. The generated models can be used as traffic rate predictors, during the network operation phase (online traffic modeling), or as video generators for estimating the network resources, during the network design phase (offline traffic modeling). In this paper, an adaptable neural-network architecture is proposed covering both cases. The scheme is based on an efficient recursive weight estimation algorithm, which adapts the network response to current conditions. In particular, the algorithm updates the network weights so that 1) the network output, after the adaptation, is approximately equal to current bit rates (current traffic statistics) and 2) a minimal degradation over the obtained network knowledge is provided. It can be shown that the proposed adaptable neuralnetwork architecture simulates a recursive nonlinear autoregressive model (RNAR) similar to the notation used in the linear case. The algorithm presents low computational complexity and high efficiency in tracking traffic rates in contrast to conventional retraining schemes. Furthermore, for the problem of offline traffic modeling, a novel correlation mechanism is proposed for capturing the burstness of the actual MPEG video traffic. The performance of the model is evaluated using several real-life MPEG coded video sources of long duration and compared with other linear/nonlinear techniques used for both cases. The results indicate that the proposed adaptable neural-network architecture presents better perform..

Non-Linear Relevance Feedback: Improving the Performance of Content-Based Retrieval Systems

In this paper, a non-linear relevance feedback mechanism is proposed for increasing the performance and the reliability of content-based retrieval systems. In particular, the human is considered as part of the retrieval process in an interactive framework, who evaluates the results provided by the system so that the system automatically updated its performance based on the users' feedback. An adaptively trained neural network architecture is used for implementing the non- linear feedback. The weight adaptation is performed in such a way that the network output satisfies the users' selection as much as possible, while simultaneously providing a minimal degradation over all previous data. Experimental results indicates that the proposed method yields better performance compared to linear relevance feedback mechanism