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End-to-end Saliency-Guided Deep image Retrieval 27th

End-to-end Saliency-Guided Deep Image Retrieval

引用

27th International conference on Neural Information processing

作者： Ma, Jinyu Gu, Xiaodong Fudan Univ Dept Elect Engn Shanghai 200433 Peoples R China

ISBN: (纸本)9783030638191;9783030638207

A challenging issue of content-based image retrieval (CBIR) is to distinguish the target object from cluttered backgrounds, resulting in more discriminative image embeddings, compared to situations where feature extraction is distracted by irrelevant objects. To handle the issue, we propose a saliency-guided model with deep image features. The model is fully based on convolution neural networks (CNNs) and it incorporates a visual saliency detection module, making saliency detection a preceding step of feature extraction. The resulted saliency maps are utilized to refine original inputs and then compatible image features suitable for ranking are extracted from refined inputs. The model suggests a working scheme of involving saliency information into existing CNN-based CBIR systems with minimum impacts on the them. Some work assist image retrieval with other methods like object detection or semantic segmentation, but they are not so fine-grained as saliency detection, meanwhile some of them require additional annotations to train. In contrast, we train the saliency module in weak-supervised end-to-end style and do not need saliency ground truth. Extensive experiments are conducted on standard image retrieval benchmarks and our model shows competitive retrieval results.

关键词： Content-based image retrieval Saliency detection Convolutional neural network Weak-supervised

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Food Cuisine Classification by Convolutional Neural Network based Transfer Learning Approach

Food Cuisine Classification by Convolutional Neural Network ...

引用

Mobile Networks and Wireless communications (ICMNWC), IEEE International conference on

作者： Priyadarshini C. Patil Vishwanath C. Burkapalli P D A College of Engg. Kalaburgi India

ISBN: (纸本)9781665446075

Food image classification is considered as a one of the uplift applications of visual food object recognition in the area of food image processing. Deep learning provides great outcomes in various challenging domains with multiple layers to constitute the inattention of data to build computational models. With this success, many studies have put forward deep-learning-based food image classification models and attained better performances collated with conventional machine learning models. We proposed a deep CNN-based food classification method for food identification with transfer learning and the fine-tuning based on the ResNet and InceptionV3 models. Comparisons of both networks are performed with sixteen and three classes of own Indian food image datasets. Inception V3 achieved more accuracy compared to ResNet-50 when more numbers of food image classes are considered.

关键词： Measurement Wireless communication visualization Computational modeling image processing Transfer learning Convolutional neural networks

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Flame image Segmentation Algorithm Based on Motion and Color Saliency

Flame Image Segmentation Algorithm Based on Motion and Color...

引用

7th International conference on communications, Signal processing, and Systems (CSPS)

作者： Li, Yuyan Wang, Lin Dalian Maritime Univ Acad Informat Sci & Technol Dalian 116026 Peoples R China

ISBN: (纸本)9789811365041;9789811365034

This paper proposed a flame segmentation algorithm based on the saliency of motion and color. First, feature point detection is performed on the video image using the scale-invariant feature transform (SIFT) algorithm, and the optical flow field of moving object in the adjacent frame is acquired by the optical flow method. According to the optical flow vector difference between the target pixel point and the surrounding neighborhood pixels, the motion saliency map is obtained based on the Munsell color system. Then, the LSI flame color statistical model based on the Lab and HSI space is used to extract color saliency map of video images. Finally, under the Bayes framework, the motion saliency map and the color saliency map are fused in an interactive manner to obtain the final flame segmentation map. Experimental results show that the proposed algorithm can effectively segment the flame image in different scenarios.

关键词： visual saliency Flame segmentation Optical flow LSI color statistics model

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Benign and Malignant Dermatoscopy image Classification 3

Benign and Malignant Dermatoscopy Image Classification

引用

3rd International conference on Advances in Electronics, Computers and communications, ICAECC 2020

作者： Christy Bobby, T. Ramaiah University of Applied Sciences-Bangalore Department of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9781728191836

Skin cancer is one of the life threatening diseases and there is mostly no chance of remission from skin cancer if diagnosed in the last stage. The three major types of skin cancers are basal cell carcinoma, squamous cell carcinoma, and melanoma. Among these three, melanoma skin cancer is dangerous and acute in nature. Dermatologists use various techniques for diagnosing the malignant, in which the popular and reliable clinical method is dermatoscopy. The manual inference of the disease condition from the dermatoscopy images requires intensive knowledge and experience in the related field. Also, there will be an unavoidable degree of variability in image analysis occurs as long as the diagnostics procedure relies on human visual perception. Thus recently, image processing and machine learning algorithms have been applied for the accurate diagnoses of skin cancers from the dermatoscopic images. Thus, the goal of the proposed work is to automatically segment and classify the dermatoscopy skin lesion image with the help of image processing and machine learning algorithms. The proposed approach classifies the skin lesion image as benign or malignant melanoma with 90% accuracy, 91% sensitivity, 86% specificity, and 93% precision. © 2020 IEEE.

关键词： Diseases

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Microscopic Traffic Monitoring and Data Collection Cloud Platform Based on Aerial Video

Microscopic Traffic Monitoring and Data Collection Cloud Pla...

引用

IEEE Wireless communications and Networking conference (IEEE WCNC)

作者： Chen, Guan-Wen Yeh, Tzu-Chuan Liu, Ching-Yu Ik, Tsi-Ui Natl Chiao Tung Univ Dept Comp Sci Coll Comp Sci 1001 Univ Rd Hsinchu 30010 Taiwan

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131061

Real-time traffic video streaming, such as roadside surveillance and aerial video, has been widely used in traffic monitoring nowadays. However, most of the traditional traffic data collection methods lack mobility that can only collect macroscopic data. In this paper, an intelligent traffic monitoring system based on an open source cooperative platform called SAGE2 was developed. Based on the integrated big screen TV wall of SAGE2, a map-based aerial traffic video streaming management interface was designed. In the image pre-processing section, it provides functions such as lens distortion removal, top view projection transforms, and video stabilization;simulate video streaming to provide instant and long-term micro-flow data collection. Micro-traffic flow data provides high-resolution information both in time and space which can be used to analyze the driving behavior of individuals and the public. Combined with the lane level map, it can provide a variety of visual vehicle flow presentations, such as intersection traffic distribution that can also be used to develop an innovative application in the future.

关键词： web-based platform SAGE2 image pre-processing

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Decision-Making Systems Based on Semantic image Analysis 1

引用

10th International Scientific and Technical conference on Open Semantic Technologies for Intelligent System, OSTIS 2020

作者： Iskra, Natallia Iskra, Vitali Lukashevich, Marina Belarusian State University of Informatics and Radioelectronics Minsk Belarus Omnigon Communications LLC New YorkNY United States

In this paper principles of decision-making systems construction are considered. An approach to image analysis based on semantic model is proposed and studied. The results show an improvement in processing speed and i... 详细信息

ISBN: (数字)9783030604479

ISBN: (纸本)9783030604462

关键词： Decision making

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Research on image Retrieval Based on Wavelet Denoising in visual Indoor Positioning Algorithm 8th

Research on Image Retrieval Based on Wavelet Denoising in Vi...

引用

8th International conference on communications, Signal processing, and Systems, CSPS 2019

作者： Wang, Zhonghong Wang, Guoqiang Zhang, Guoying Heilongjiang University HarbinHeilongjiang China Key Lab of Electronic and Communication Engineering Heilongjiang University No. 74 Xuefu Road Harbin China

ISBN: (纸本)9789811394089

For the problem of image matching in visual indoor positioning research, the image quality is degraded due to the interference and influence of noises during the generation or transmission of the image, which ultimately leads to the problem of image matching rate and low efficiency. Comparing existing traditional denoising algorithms and wavelet transform algorithms, we use MATLAB for simulation to compare the effects of adding different noises and denoising with different wavelet bases. The results show that the wavelet image denoising improves the shortcomings of the traditional denoising algorithm to a certain extent, but there is still room for improvement. © 2020, Springer Nature Singapore Pte Ltd.

关键词： image denoising

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Multi-scale Attention Consistency for Multi-label image Classification 27th

Multi-scale Attention Consistency for Multi-label Image Clas...

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27th International conference on Neural Information processing

作者： Xu, Haotian Jin, Xiaobo Wang, Qiufeng Huang, Kaizhu Xian Jiaotong Liverpool Univ Sch Adv Technol Dept Intelligent Sci Suzhou Peoples R China

ISBN: (纸本)9783030638191;9783030638207

Human has well demonstrated its cognitive consistency over image transformations such as flipping and scaling. In order to learn from human's visual perception consistency, researchers find out that convolutional neural network's capacity of discernment can be further elevated via forcing the network to concentrate on certain area in the picture in accordance with the human natural visual perception. Attention heatmap, as a supplementary tool to reveal the essential region that the network chooses to focus on, has been developed and widely adopted by CNNs. Based on this regime of visual consistency, we propose a novel end-to-end trainable CNN architecture with multi-scale attention consistency. Specifically, our model takes an original picture and its flipped counterpart as inputs, and then send them into a single standard Resnet with additional attention-enhanced modules to generate a semantically strong attention heatmap. We also compute the distance between multi-scale attention heatmaps of these two pictures and take it as an additional loss to help the network achieve better performance. Our network shows superiority on the multi-label classification task and attains compelling results on the WIDER Attribute Dataset.

关键词： image classification Multi-label learning Attention Consistency

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Correcting Low-Illumination images Using Multi-Scale Fusion in a Pyramidal Framework 5

Correcting Low-Illumination Images Using Multi-Scale Fusion ...

引用

5th International conference on Wireless communications Signal processing and Networking (IEEE WiSPNET)

作者： Das, Swadhin Roy, Manali Mukhopadhyay, Susanta Indian Inst Technol ISM Dhanbad Dept Comp Sci & Engn Dhanbad Bihar India

ISBN: (纸本)9781728152844

In this work, the authors have proposed a method for improving the visual quality of color images suffering from low illumination. In this pyramid based multiscale technique, the input image is decomposed into four different levels of resolutions. Starting from the coarsest resolution, each image is converted to HSV space, and the illumination is computed from the V component employing multiscale Gaussian function. At this stage, Weber-Fechner law is used to construct two enhanced versions of the illumination component corresponding to two different values of a parameter. These two images are fused using PCA to construct a new V image, which is subsequently super-sampled to its next higher level of resolution. The V images of the next higher level of resolution are subjected to the same treatment until we reach the base level of the pyramid. Finally, all the V component images at the base level are fused employing PCA to construct the final resultant V component, which in turn is combined with the H and S components to construct the final result. The method has been implemented and tested on a set of real 2D color images, and the results are found satisfactory. The experimental results have been compared with those of other methods based on some objective measures.

关键词： Adaptive enhancement image pyramid Multi-scale Gaussian function Weber-Fechner law PCA

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ATSal: An Attention Based Architecture for Saliency Prediction in 360 ∘ Videos 25th

ATSal: An Attention Based Architecture for Saliency Predicti...

引用

25th International conference on Pattern Recognition Workshops, ICPR 2020

作者： Dahou, Yasser Tliba, Marouane McGuinness, Kevin O’Connor, Noel Insight Centre for Data Analytics Dublin City University Dublin 9 Ireland Institut National des Télécommunications et des TIC Oran Algeria

ISBN: (纸本)9783030687953

The spherical domain representation of 360 ∘ video/image presents many challenges related to the storage, processing, transmission and rendering of omnidirectional videos (ODV). Models of human visual attention can be used so that only a single viewport is rendered at a time, which is important when developing systems that allow users to explore ODV with head mounted displays (HMD). Accordingly, researchers have proposed various saliency models for 360 ∘ video/images. This paper proposes ATSal, a novel attention based (head-eye) saliency model for 360 ∘ videos. The attention mechanism explicitly encodes global static visual attention allowing expert models to focus on learning the saliency on local patches throughout consecutive frames. We compare the proposed approach to other state-of-the-art saliency models on two datasets: Salient360! and VR-EyeTracking. Experimental results on over 80 ODV videos (75K+ frames) show that the proposed method outperforms the existing state-of-the-art. © 2021, Springer Nature Switzerland AG.

关键词： Helmet mounted displays

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