scholarly journals A Novel Saliency Detection Method for Wild Animal Monitoring Images with WMSN

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Wenzhao Feng ◽  
Junguo Zhang ◽  
Chunhe Hu ◽  
Yuan Wang ◽  
Qiumin Xiang ◽  
...  
Keyword(s):  
Detection Method ◽  
Detection Efficiency ◽  
Saliency Detection ◽  
Saliency Map ◽  
Wild Animal ◽  
Image Texture ◽  
Detection Methods ◽  
Wireless Multimedia ◽  
Animal Images ◽  
Animal Monitoring

We proposed a novel saliency detection method based on histogram contrast algorithm and images captured with WMSN (wireless multimedia sensor network) for practical wild animal monitoring purpose. Current studies on wild animal monitoring mainly focus on analyzing images with high resolution, complex background, and nonuniform illumination features. Most current visual saliency detection methods are not capable of completing the processing work. In this algorithm, we firstly smoothed the image texture and reduced the noise with the help of structure extraction method based on image total variation. After that, the saliency target edge information was obtained by Canny operator edge detection method, which will be further improved by position saliency map according to the Hanning window. In order to verify the efficiency of the proposed algorithm, field-captured wild animal images were tested by using our algorithm in terms of visual effect and detection efficiency. Compared with histogram contrast algorithm, the result shows that the rate of average precision, recall and F-measure improved by 18.38%, 19.53%, 19.06%, respectively, when processing the captured animal images.

BSFCoS: Block and Sparse Principal Component Analysis-Based Fast Co-Saliency Detection Method

2015 ◽  
Vol 30 (01) ◽  
pp. 1655003 ◽  
Author(s):  
Ning-Min Shen ◽  
Jing Li ◽  
Pei-Yun Zhou ◽  
Ying Huo ◽  
Yi Zhuang
Keyword(s):  
Detection Method ◽  
Feature Fusion ◽  
Rapid Development ◽  
Saliency Detection ◽  
Principal Component ◽  
Saliency Map ◽  
Detection Methods ◽  
Image Block ◽  
Feature Extraction Method ◽  
Sparse Features

Co-saliency detection, an emerging research area in saliency detection, aims to extract the common saliency from the multi images. The extracted co-saliency map has been utilized in various applications, such as in co-segmentation, co-recognition and so on. With the rapid development of image acquisition technology, the original digital images are becoming more and more clearly. The existing co-saliency detection methods processing these images need enormous computer memory along with high computational complexity. These limitations made it hard to satisfy the demand of real-time user interaction. This paper proposes a fast co-saliency detection method based on the image block partition and sparse feature extraction method (BSFCoS). Firstly, the images are divided into several uniform blocks, and the low-level features are extracted from Lab and RGB color spaces. In order to maintain the characteristics of the original images and reduce the number of feature points as well as possible, Truncated Power for sparse principal components method are employed to extract sparse features. Furthermore, K-Means method is adopted to cluster the extracted sparse features, and calculate the three salient feature weights. Finally, the co-saliency map was acquired from the feature fusion of the saliency map for single image and multi images. The proposed method has been tested and simulated on two benchmark datasets: Co-saliency Pairs and CMU Cornell iCoseg datasets. Compared with the existing co-saliency methods, BSFCoS has a significant running time improvement in multi images processing while ensuring detection results. Lastly, the co-segmentation method based on BSFCoS is also given and has a better co-segmentation performance.

scholarly journals Ship Detection in Panchromatic Optical Remote Sensing Images Based on Visual Saliency and Multi-Dimensional Feature Description

2020 ◽  
Vol 12 (1) ◽  
pp. 152 ◽  
Author(s):  
Ting Nie ◽  
Xiyu Han ◽  
Bin He ◽  
Xiansheng Li ◽  
Hongxing Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Fourier Transform ◽  
Detection Method ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Saliency Map ◽  
False Alarms ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Ship Detection

Ship detection in panchromatic optical remote sensing images is faced with two major challenges, locating candidate regions from complex backgrounds quickly and describing ships effectively to reduce false alarms. Here, a practical method was proposed to solve these issues. Firstly, we constructed a novel visual saliency detection method based on a hyper-complex Fourier transform of a quaternion to locate regions of interest (ROIs), which can improve the accuracy of the subsequent discrimination process for panchromatic images, compared with the phase spectrum quaternary Fourier transform (PQFT) method. In addition, the Gaussian filtering of different scales was performed on the transformed result to synthesize the best saliency map. An adaptive method based on GrabCut was then used for binary segmentation to extract candidate positions. With respect to the discrimination stage, a rotation-invariant modified local binary pattern (LBP) description was achieved by combining shape, texture, and moment invariant features to describe the ship targets more powerfully. Finally, the false alarms were eliminated through SVM training. The experimental results on panchromatic optical remote sensing images demonstrated that the presented saliency model under various indicators is superior, and the proposed ship detection method is accurate and fast with high robustness, based on detailed comparisons to existing efforts.

scholarly journals A Self-Learning Detection Method of Sybil Attack Based on LSTM for Electric Vehicles

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1382 ◽  
Author(s):  
Yi-Ying Zhang ◽  
Jing Shang ◽  
Xi Chen ◽  
Kun Liang
Keyword(s):  
Communication Network ◽  
Electric Vehicles ◽  
Short Term Memory ◽  
Detection Method ◽  
Detection Efficiency ◽  
Attack Detection ◽  
Detection Methods ◽  
Sybil Attack ◽  
Time Dimension ◽  
Self Learning

Electric vehicles (EVs) are the development direction of new energy vehicles in the future. As an important part of the Internet of things (IOT) communication network, the charging pile is also facing severe challenges in information security. At present, most detection methods need a lot of prophetic data and too much human intervention, so they cannot do anything about unknown attacks. In this paper, a self-learning-based attack detection method is proposed, which makes training and prediction a closed-loop system according to a large number of false information packets broadcast to the communication network. Using long short-term memory (LSTM) neural network training to obtain the characteristics of traffic data changes in the time dimension, the unknown malicious behavior characteristics are self-extracted and self-learning, improving the detection efficiency and quality. In this paper, we take the Sybil attack in the car network as an example. The simulation results show that the proposed method can detect the Sybil early attack quickly and accurately.

Motion Saliency Detection in Videos

2014 ◽  
Vol 644-650 ◽  
pp. 4603-4606
Author(s):  
Quan Quan Wan
Keyword(s):  
Detection Method ◽  
Saliency Detection ◽  
Saliency Map ◽  
Experimental Results ◽  
The Other ◽  
Video Sequences ◽  
Video Frame ◽  
Video Scene ◽  
Salient Regions ◽  
Motion Saliency

In order to detect visually salient regions in video sequences, a motion saliency detection method is proposed. The motion vectors of each video frame is used to get two motion saliency features. One represents the uniqueness of the motion,and the other one represents the distribution of the motion in the video scene. Then, the Gaussian filtering is conducted to combine the two feature to make the motion saliency map, in which the salient regions or objects in the video sequences could be detected. The experimental results show that the proposed method could achieve excellent saliency detection performances.

scholarly journals LSTM-Based Hierarchical Denoising Network for Android Malware Detection

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Jinpei Yan ◽  
Yong Qi ◽  
Qifan Rao
Keyword(s):  
Detection Method ◽  
Expert Knowledge ◽  
Detection Efficiency ◽  
Malware Detection ◽  
Mobile Security ◽  
Detection Methods ◽  
Android Malware ◽  
Android Malware Detection ◽  
Malicious Behavior ◽  
Gradient Scaling

Mobile security is an important issue on Android platform. Most malware detection methods based on machine learning models heavily rely on expert knowledge for manual feature engineering, which are still difficult to fully describe malwares. In this paper, we present LSTM-based hierarchical denoise network (HDN), a novel static Android malware detection method which uses LSTM to directly learn from the raw opcode sequences extracted from decompiled Android files. However, most opcode sequences are too long for LSTM to train due to the gradient vanishing problem. Hence, HDN uses a hierarchical structure, whose first-level LSTM parallelly computes on opcode subsequences (we called them method blocks) to learn the dense representations; then the second-level LSTM can learn and detect malware through method block sequences. Considering that malicious behavior only appears in partial sequence segments, HDN uses method block denoise module (MBDM) for data denoising by adaptive gradient scaling strategy based on loss cache. We evaluate and compare HDN with the latest mainstream researches on three datasets. The results show that HDN outperforms these Android malware detection methods,and it is able to capture longer sequence features and has better detection efficiency than N-gram-based malware detection which is similar to our method.

scholarly journals The Study of Randomized Visual Saliency Detection Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Yuantao Chen ◽  
Weihong Xu ◽  
Fangjun Kuang ◽  
Shangbing Gao
Keyword(s):  
Image Segmentation ◽  
Detection Method ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Detection Algorithm ◽  
Saliency Map ◽  
Input Image ◽  
Memory Space ◽  
Segmentation Process ◽  
Visual Saliency Detection

Image segmentation process for high quality visual saliency map is very dependent on the existing visual saliency metrics. It is mostly only get sketchy effect of saliency map, and roughly based visual saliency map will affect the image segmentation results. The paper had presented the randomized visual saliency detection algorithm. The randomized visual saliency detection method can quickly generate the same size as the original input image and detailed results of the saliency map. The randomized saliency detection method can be applied to real-time requirements for image content-based scaling saliency results map. The randomization method for fast randomized video saliency area detection, the algorithm only requires a small amount of memory space can be detected detailed oriented visual saliency map, the presented results are shown that the method of visual saliency map used in image after the segmentation process can be an ideal segmentation results.

scholarly journals Multi-Scale Adversarial Feature Learning for Saliency Detection

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 457 ◽  
Author(s):  
Dandan Zhu ◽  
Lei Dai ◽  
Ye Luo ◽  
Guokai Zhang ◽  
Xuan Shao ◽  
...  
Keyword(s):  
Saliency Detection ◽  
Feature Learning ◽  
Ground Truth ◽  
Saliency Map ◽  
Natural Images ◽  
Learning Ability ◽  
Detection Methods ◽  
Generative Adversarial Network ◽  
Great Ability ◽  
Multi Scale

Previous saliency detection methods usually focused on extracting powerful discriminative features to describe images with a complex background. Recently, the generative adversarial network (GAN) has shown a great ability in feature learning for synthesizing high quality natural images. Since the GAN shows a superior feature learning ability, we present a new multi-scale adversarial feature learning (MAFL) model for image saliency detection. In particular, we build this model, which is composed of two convolutional neural network (CNN) modules: the multi-scale G-network takes natural images as inputs and generates the corresponding synthetic saliency map, and we design a novel layer in the D-network, namely a correlation layer, which is used to determine whether one image is a synthetic saliency map or ground-truth saliency map. Quantitative and qualitative comparisons on several public datasets show the superiority of our approach.

scholarly journals Thermal Infrared Small Ship Detection in Sea Clutter Based on Morphological Reconstruction and Multi-Feature Analysis

2019 ◽  
Vol 9 (18) ◽  
pp. 3786 ◽  
Author(s):  
Yongsong Li ◽  
Zhengzhou Li ◽  
Yong Zhu ◽  
Bo Li ◽  
Weiqi Xiong ◽  
...  
Keyword(s):  
Saliency Detection ◽  
Thermal Infrared ◽  
Saliency Map ◽  
Detection Methods ◽  
Feature Analysis ◽  
Sea Clutter ◽  
Morphological Reconstruction ◽  
Ship Detection ◽  
Statistical Shape ◽  
Small Ship

The existing thermal infrared (TIR) ship detection methods may suffer serious performance degradation in the situation of heavy sea clutter. To cope with this problem, a novel ship detection method based on morphological reconstruction and multi-feature analysis is proposed in this paper. Firstly, the TIR image is processed by opening- or closing-based gray-level morphological reconstruction (GMR) to smooth intricate background clutter while maintaining the intensity, shape, and contour features of ship target. Then, considering the intensity and contrast features, the fused saliency detection strategy including intensity foreground saliency map (IFSM) and brightness contrast saliency map (BCSM) is presented to highlight potential ship targets and suppress sea clutter. After that, an effective contour descriptor namely average eigenvalue measure of structure tensor (STAEM) is designed to characterize candidate ship targets, and the statistical shape knowledge is introduced to identify true ship targets from residual non-ship targets. Finally, the dual method is adopted to simultaneously detect both bright and dark ship targets in TIR image. Extensive experiments show that the proposed method outperforms the compared state-of-the-art methods, especially for infrared images with intricate sea clutter. Moreover, the proposed method can work stably for ship target with unknown brightness, variable quantities, sizes, and shapes.

Dynamic Zero-Parallax-Setting Techniques for Multi-View Autostereoscopic Display

2020 ◽  
Vol 2020 (2) ◽  
pp. 98-1-98-6
Author(s):  
Yuzhong Jiao ◽  
Mark Ping Chan Mok ◽  
Kayton Wai Keung Cheung ◽  
Man Chi Chan ◽  
Tak Wai Shen ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Deep Learning ◽  
Saliency Detection ◽  
Depth Map ◽  
Visual Saliency ◽  
Saliency Map ◽  
Detection Methods ◽  
3D Vision ◽  
Detection Techniques ◽  
Autostereoscopic Displays

The objective of this paper is to research a dynamic computation of Zero-Parallax-Setting (ZPS) for multi-view autostereoscopic displays in order to effectively alleviate blurry 3D vision for images with large disparity. Saliency detection techniques can yield saliency map which is a topographic representation of saliency which refers to visually dominant locations. By using saliency map, we can predict what attracts the attention, or region of interest, to viewers. Recently, deep learning techniques have been applied in saliency detection. Deep learning-based salient object detection methods have the advantage of highlighting most of the salient objects. With the help of depth map, the spatial distribution of salient objects can be computed. In this paper, we will compare two dynamic ZPS techniques based on visual attention. They are 1) maximum saliency computation by Graphic-Based Visual Saliency (GBVS) algorithm and 2) spatial distribution of salient objects by a convolutional neural networks (CNN)-based model. Experiments prove that both methods can help improve the 3D effect of autostereoscopic displays. Moreover, the spatial distribution of salient objects-based dynamic ZPS technique can achieve better 3D performance than maximum saliency-based method.

scholarly journals Salient Region Detection Using Diffusion Process with Nonlocal Connections

2018 ◽  
Vol 8 (12) ◽  
pp. 2526 ◽  
Author(s):  
Huiyuan Luo ◽  
Guangliang Han ◽  
Peixun Liu ◽  
Yanfeng Wu
Keyword(s):  
Saliency Detection ◽  
Gaussian Model ◽  
Single Layer ◽  
Saliency Map ◽  
Detection Methods ◽  
Sparse Graph ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection ◽  
Benchmark Datasets

Diffusion-based salient region detection methods have gained great popularity. In most diffusion-based methods, the saliency values are ranked on 2-layer neighborhood graph by connecting each node to its neighboring nodes and the nodes sharing common boundaries with its neighboring nodes. However, only considering the local relevance between neighbors, the salient region may be heterogeneous and even wrongly suppressed, especially when the features of salient object are diverse. In order to address the issue, we present an effective saliency detection method using diffusing process on the graph with nonlocal connections. First, a saliency-biased Gaussian model is used to refine the saliency map based on the compactness cue, and then, the saliency information of compactness is diffused on 2-layer sparse graph with nonlocal connections. Second, we obtain the contrast of each superpixel by restricting the reference region to the background. Similarly, a saliency-biased Gaussian refinement model is generated and the saliency information based on the uniqueness cue is propagated on the 2-layer sparse graph. We linearly integrate the initial saliency maps based on the compactness and uniqueness cues due to the complementarity to each other. Finally, to obtain a highlighted and homogeneous saliency map, a single-layer updating and multi-layer integrating scheme is presented. Comprehensive experiments on four benchmark datasets demonstrate that the proposed method performs better in terms of various evaluation metrics.

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