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Acoustic classification of frogs has received increasing attention for its promising application in ecological studies. Various studies have been proposed for classifying frog species, but most recordings are assumed to have only a single species. In this study, a method to classify multiple frog species in an audio clip is presented. To be specific, continuous frog recordings are first cropped into...
Urban change detection is an important part of monitoring operations and disaster relief efforts. However, often sufficient ground truth data is not available to use traditional supervised machine learning techniques. In this paper, a novel Deep Learning based weakly-supervised framework for urban change detection using multi-temporal polarimetric SAR data is proposed. A modified unsupervised stacked...
In this paper, a novel model of Gabor Filtering based Deep Network (GFDN) for hyperspectral image classification is proposed. First, spatial features are extracted via Gabor filtering from the three principal components. Gabor filter can capture physical structures of hyperspectral images, such as specific orientation information. Then, the Gabor features and spectral features are simply staked to...
This paper investigates whether advanced neural network techniques can be applied to the detection and identification of typical targets in the context of land warfare. We collected 13 typical targets and built a detection data set. Based on the Faster R-CNN framework, we improve the detection accuracy by two ways. First, we design a neural network model with strong local modeling capabilities. Second,...
This paper proposes the use of Stacked Denoising Autoencoder to predict the direction of movement of stock indexes based on the historical and volume data of the underlying stocks. The Stacked Denoising Autoencoder is a deep learning method widely used in the field of computer vision which is capable of learning a compact feature representation of the data for stock index prediction. The Hybrid Gravitational...
This study uses remote sensing technology that can provide information about the condition of the earth's surface area, fast, and spatially. The study area was in Karawang District, lying in the Northern part of West Java-Indonesia. We address a paddy growth stages classification using LANDSAT 8 image data obtained from multi-sensor remote sensing image taken in October 2015 to August 2016. This study...
In this study, we present a new phoneme-based deep neural network (DNN) framework for single microphone speech enhancement. While most speech enhancement algorithms overlook the phoneme structure of the speech signal, our proposed framework comprises a set of phoneme-specific DNNs (pDNNs), one for each phoneme, together with an additional phoneme-classification DNN (cDNN). The cDNN is responsible...
In this paper, we propose a principled framework for pornographic image recognition. Specifically, we present our definition of pornographic images, which characterizes the pornographic contents in images as the exposure of private body parts. As the private body parts often lie in local image regions, we model each image as a bag of local image patches (instances), and assume that for each pornographic...
Recent advances in deep learning made it possible to build deep hierarchical models capable of delivering state-of-the-art performance in various vision tasks, such as object recognition, detection or tracking. For recognition tasks the most common approach when using deep models is to learn object representations (or features) directly from raw image-input and then feed the learned features to a...
Toward the goal of improved representation learning, we propose a novel deep architecture for unsupervised feature learning based on a recursive multilayered union of subspaces (UoS) model. The model is able to accurately generate recursive nested signal segments at increasing fields of view as we progress from one layer to the next. The local subspace dimension (latent space) grows linearly while...
Water quality assessment is very important for monitoring water sources and main canal, which is beneficial to offer strategies for the management of water quality and environment. This paper proposes a water quality assessment method based on a sparse autoencoder network. In the proposed approach, a representation model is firstly learned via a sparse autoencoder trained by unlabeled water monitoring...
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