On the Application of Data Clustering Algorithm used in Information Retrieval for Satellite Imagery Segmentation

This study proposes an automated technique for segmenting satellite imagery using unsupervised learning. Autoencoders, a type of neural network, are employed for dimensionality reduction and feature extraction. The study evaluates different segmentation architectures and encoders and identifies the best performing combination as the DeepLabv3+ architecture with a ResNet-152 encoder. This approach achieves high performance scores across multiple metrics and can be beneficial in various fields, including agriculture, land use monitoring, and disaster response

Performance Measure of Scanned Tamil Land Documents using Neural Network Approach

Recognition is process to find out the noisy or distorted image to make an accurate image. Classification and recognition technique which implemented on scanned Tamil land document. In the pre-processing stage, the given dataset is filtered by using median filter. After that, segmentation process is applied for every word image is splitted into character. Then, feature extraction is done by Gabor wavelet. For post processing stage, classification is process to check out the dataset which using neural network technique like supervised learning method or unsupervised learning method to find out the correct and Incorrect classification measure using confusion matrix. Hence, finally implemented Gabor wavelet technique to find the feature extraction and selection and then classification is done new pattern recognition technique using MATLAB. We also find out performance of plotting function like Training state, Regression, Gradient and validation. DOI: 10.17762/ijritcc2321-8169.150519

Bio-Inspired Multi-Layer Spiking Neural Network Extracts Discriminative Features from Speech Signals

Spiking neural networks (SNNs) enable power-efficient implementations due to their sparse, spike-based coding scheme. This paper develops a bio-inspired SNN that uses unsupervised learning to extract discriminative features from speech signals, which can subsequently be used in a classifier. The architecture consists of a spiking convolutional/pooling layer followed by a fully connected spiking layer for feature discovery. The convolutional layer of leaky, integrate-and-fire (LIF) neurons represents primary acoustic features. The fully connected layer is equipped with a probabilistic spike-timing-dependent plasticity learning rule. This layer represents the discriminative features through probabilistic, LIF neurons. To assess the discriminative power of the learned features, they are used in a hidden Markov model (HMM) for spoken digit recognition. The experimental results show performance above 96% that compares favorably with popular statistical feature extraction methods. Our results provide a novel demonstration of unsupervised feature acquisition in an SNN

Multi-modal Medical Neurological Image Fusion using Wavelet Pooled Edge Preserving Autoencoder

Medical image fusion integrates the complementary diagnostic information of the source image modalities for improved visualization and analysis of underlying anomalies. Recently, deep learning-based models have excelled the conventional fusion methods by executing feature extraction, feature selection, and feature fusion tasks, simultaneously. However, most of the existing convolutional neural network (CNN) architectures use conventional pooling or strided convolutional strategies to downsample the feature maps. It causes the blurring or loss of important diagnostic information and edge details available in the source images and dilutes the efficacy of the feature extraction process. Therefore, this paper presents an end-to-end unsupervised fusion model for multimodal medical images based on an edge-preserving dense autoencoder network. In the proposed model, feature extraction is improved by using wavelet decomposition-based attention pooling of feature maps. This helps in preserving the fine edge detail information present in both the source images and enhances the visual perception of fused images. Further, the proposed model is trained on a variety of medical image pairs which helps in capturing the intensity distributions of the source images and preserves the diagnostic information effectively. Substantial experiments are conducted which demonstrate that the proposed method provides improved visual and quantitative results as compared to the other state-of-the-art fusion methods.Comment: 8 pages, 5 figures, 6 table