Batik image retrieval using convolutional neural network

This paper presents a simple technique for performing Batik image retrieval using the Convolutional Neural Network (CNN) approach. Two CNN models, i.e. supervised and unsupervised learning approach, are considered to perform end-to-end feature extraction in order to describe the content of Batik image. The distance metrics measure the similarity between the query and target images in database based on the feature generated from CNN architecture. As reported in the experimental section, the proposed supervised CNN model achieves better performance compared to unsupervised CNN in the Batik image retrieval system. In addition, image feature composed from the proposed CNN model yields better performance compared to that of the handcrafted feature descriptor. Yet, it demonstrates the superiority performance of deep learning-based approach in the Batik image retrieval system

NOISY BAND SELECTION BASED ON THE INTEGRATION OF THE STACKED-AUTOENCODER AND CONVOLUTIONAL NEURAL NETWORK APPROACHES FOR HYPERSPECTRAL DATA: Seleção de bandas ruidosas baseada na integração de Stacked-AutoEncoder e redes neurais convulacionais para dados hiperespectrais

The presence of noise on hyperspectral images causes degradation and hinders efficiency of processing for land cover classification. In this sense, removing noise or detecting noisy bands automatically on hyperspectral images becomes a challenge for research in remote sensing. To cope this problem, an integrated model (SAE-1DCNN) is presented in this study, based on Stacked-Autoencoders (SAE) and Convolutional Neural Networks (CNN) algorithms for the selection and exclusion of noisy bands. The proposed model employs convolutional layers to improve the performance of autoencoders focused on discriminating the training data by analyzing the hyperspectral signature of the pixel. Thus, in the SAE-1DCNN model, information can be compressed, and then redundant information can be detected and extracted by taking advantage of the efficiency of the deep architecture based on the convolutional and pooling layers. Hyperspectral data from the AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) sensor were used to evaluate the performance of the proposed automatic method based on feature selection. The results showed effectiveness to identify noisy bands automatically, suggesting that the proposed methodology was found to be promising and can be an alternative to identify noisy bands within the scope of hyperspectral data pre-processing. Keywords: noisy bands; feature selection; convolutional neural network; stacked-autoencoders; hyperspectral dataRESUMO - A presença de ruído em imagens hiperespectrais causa degradação e dificulta a eficiência no processamento para a classificação da cobertura terrestre. Nesse sentido, a remoção do ruído ou a detecção automática de bandas ruidosas em imagens hiperespectrais torna-se um desafio para pesquisas na área de sensoriamento remoto. Para solucionar esse problema, um modelo integrado (SAE-1DCNN) é apresentado nesse estudo, baseado nos algoritmos de Deep Learning conhecidos como: Stacked-Autoencoders (SAE) e Redes Neurais Convolucionais (CNN) para a seleção e exclusão de bandas ruidosas. O modelo proposto emprega as camadas convolucionais para melhorar o desempenho dos Autoencoders focados na discriminação dos dados de treinamento por meio da análise da assinatura hiperespectral do pixel. Assim, no modelo SAE-1DCNN, a informação pode ser comprimida, e depois a informação redundante pode ser detectada e extraída tirando partido da eficiência da arquitetura profunda baseada nas camadas convolucionais e de agrupamento. Os dados hiperespectrais do sensor AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) foram utilizados para avaliar o desempenho do método automático proposto, com base na seleção de atributos. Os resultados mostraram eficácia na identificação automática de bandas ruidosas, sugerindo que a metodologia proposta foi considerada promissora e pode ser uma alternativa para identificar bandas ruidosas no âmbito de pré-processamento de dados hiperespectrais. ABSTRACT - The presence of noise on hyperspectral images causes degradation and hinders efficiency of processing for land cover classification. In this sense, removing noise or detecting noisy bands automatically on hyperspectral images becomes a challenge for research in remote sensing. To cope this problem, an integrated model (SAE-1DCNN) is presented in this study, based on Stacked-Autoencoders (SAE) and Convolutional Neural Networks (CNN) algorithms for the selection and exclusion of noisy bands. The proposed model employs convolutional layers to improve the performance of autoencoders focused on discriminating the training data by analyzing the hyperspectral signature of the pixel. Thus, in the SAE-1DCNN model, information can be compressed, and then redundant information can be detected and extracted by taking advantage of the efficiency of the deep architecture based on the convolutional and pooling layers. Hyperspectral data from the AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) sensor were used to evaluate the performance of the proposed automatic method based on feature selection. The results showed effectiveness to identify noisy bands automatically, suggesting that the proposed methodology was found to be promising and can be an alternative to identify noisy bands within the scope of hyperspectral data pre-processing

Fast embedding for image classification & retrieval and its application to the hostel industry

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonContent-based image classification and retrieval are the automatic processes of taking an unseen image input and extracting its features representing the input image. Then, for the classification task, this mathematically measured input is categorized according to established criteria in the server and consequently shows the output as a result. On the other hand, for the retrieval task, the extracted features of an unseen query image are sent to the server to search for the most visually similar images to a given image and retrieve these images as a result. Despite image features could be represented by classical features, artificial intelligence-based features, Convolutional Neural Networks (CNN) to be precise, have become powerful tools in the field. Nonetheless, the high dimensional CNN features have been a challenge in particular for applications on mobile or Internet of Things devices. Therefore, in this thesis, several fast embeddings are explored and proposed to overcome the constraints of low memory, bandwidth, and power. Furthermore, the first hostel image database is created with three datasets, hostel image dataset containing 13,908 interior and exterior images of hostels across the world, and Hostels-900 dataset and Hostels-2K dataset containing 972 images and 2,380 images, respectively, of 20 London hostel buildings. The results demonstrate that the proposed fast embeddings such as the application of GHM-Rand operator, GHM-Fix operator, and binary feature vectors are able to outperform or give competitive results to those state-of-the-art methods with a lot less computational resource. Additionally, the findings from a ten-year literature review of CBIR study in the tourism industry could picturize the relevant research activities in the past decade which are not only beneficial to the hostel industry or tourism sector but also to the computer science and engineering research communities for the potential real-life applications of the existing and developing technologies in the field