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Deep Learning-Based Sentiment Analysis for Social Media 5

Deep Learning-Based Sentiment Analysis for Social Media

引用

5th international Conference on Artificial Intelligence and pattern recognition, AIPR 2022

作者： Wang, Zhe Liu, Ying Fang, Jie Li, Daxiang Center for Image and Information Processing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

Due to the continuous popularization of the Internet and mobile phones, people have gradually entered a participatory network era, and the rapid growth of social networks has caused an explosion of digital information content. It has turned online opinions, blogs, tweets and posts into highly valuable assets, allowing governments and businesses to gain insights from the data and make their strategies. Business organizations need to process and analyze these sentiments to investigate the data and gain business insights. In recent years, deep learning techniques have been very successful in performing sentiment analysis, which offers automatic feature extraction, rich representation capabilities and better performance compared with traditional feature-based techniques. the core idea is to extract complex features automatically from large amounts of data by building deep neural networks to generate up-to-date predictions. this paper reviews social media sentiment analysis methods based on deep learning. Firstly, it introduces the process of single-modal text sentiment analysis on social media. then it summarizes the multimodal sentiment analysis algorithms for social media, and divides the algorithm into feature layer fusion, decision layer fusion and linear regression model according to different fusion strategies. finally, the difficulties of social media sentiment analysis based on deep learning and future research directions are discussed. © 2022 ACM.

关键词： Sentiment analysis

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Inline Double Layer Depth Estimation with Transparent Materials 42nd

Inline Double Layer Depth Estimation with Transparent Materi...

引用

42nd German Conference on pattern recognition, DAGM GCPR 2020 held in parallel with 25th international symposium on Vision, Modeling, and Visualization, VMV 2020 and 10th Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2020

作者： Kopf, Christian Pock, thomas Blaschitz, Bernhard Štolc, Svorad Graz University of Technology Graz Austria AIT Austrian Institute of Technology GmbH Vienna Austria Photoneo s.r.o. Bratislava Slovakia

ISBN: (纸本)9783030712778

3D depth computation from stereo data has been one of the most researched topics in computer vision. While state-of-art approaches have flourished over time, reconstruction of transparent materials is still considered an open problem. Based on 3D light field data we propose a method to obtain smooth and consistent double-layer estimates of scenes with transparent materials. Our novel approach robustly combines estimates from models with different layer hypotheses in a cost volume with subsequent minimization of a joint second order TGV energy on two depth layers. Additionally we showcase the results of our approach on objects from common inspection use-cases in an industrial setting and compare our work to related methods. © 2021, Springer Nature Switzerland AG.

关键词： Stereo image processing

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image Compression with Neural Networks Using Complexity Level of images

Image Compression with Neural Networks Using Complexity Leve...

引用

international symposium on image and Signal processing and Analysis

作者： Hadi Veisi Mansour Jamzad Department of Computer Engineering Sharif University of Technology Tehran Iran

ISBN: (纸本)9531841160;97

this paper presents a complexity-based image compression method using neural networks. In this method, different multi-layer perceptron ANNs are used as compressor and de-compressor. Each image is divided into blocks, complexity of each block is computed using complexity measure methods and one network is selected for each block according to its complexity value. three complexity measure methods, called entropy, activity and pattern-based are used to determine the level of complexity in image blocks and their ability are evaluated and compared together. Selection of a network for each image block is based on its complexity value or the Best-SNR criterion. Best-SNR chooses one of the trained networks such that it results best SNR in compressing a block of input image. In our evaluations, best results, with PSNR criterion, are obtained when overlapping of blocks is allowed and choosing the networks in compressor is based on the Best-SNR criterion. In this case, the results demonstrate superiority of our method comparing with previous similar works and that of JPEG standard coding.

关键词： image coding Neural networks Artificial neural networks Multilayer perceptrons Computer networks Entropy PSNR Neurons Transform coding Memory

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A validity measure for fuzzy clustering and its use in selecting optimal number of clusters

A validity measure for fuzzy clustering and its use in selec...

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IEEE international Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Hyun-Sook Rhee Kyung-Whan Oh Department of Computer Science Artificial Intelligence Research Laboratory Sogang University South Korea

ISBN: (纸本)0780336453

Cluster analysis has been playing an important role in solving many problems in pattern recognition and image processing. If fuzzy cluster analysis is to make a significant contribution to engineering applications, much more attention must be paid to fundamental decision on the number of clusters in data. It is related to cluster validity problem of how well it has identified the structure that is present in the data. In this paper, we define I/sub G/ as a fuzzy clustering validity function which measures the overall average compactness and separation of fuzzy c-partition and propose a new approach to selecting optimal number of clusters using the measurement value of I/sub G/. this approach uses relative values and normalized value of I/sub G/ and it does not require human interpretation. It is compared with conventional validity functions, partition coefficient and CSC index, on the several data sets.

关键词： Humans Clustering algorithms Entropy pattern recognition Fuzzy set theory image analysis pattern analysis image processing Data engineering Data analysis

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Fast panoramic face mosaicing and recognition

Fast panoramic face mosaicing and recognition

引用

international symposium on image and Signal processing and Analysis

作者： Fan Yang M. Paindavoine H. Abdi Laboratoire Le2i Aile de l'Ingénieur-Mirande Université de Bourgogne France University of Texas Dallas Richardson TX USA

In this article, we present some development results of a system that performs mosaicing (or mosaicking) of panoramic faces. Our objective is to study the feasibility of panoramic face construction in real-time. To do so, we built a simple acquisition system composed of 5 standard cameras which, together, can take simultaneously 5 views of a face at different angles. then, we chose an easily hardware-achievable algorithm, consisting of successive linear transformations, in order to compose a panoramic face from these 5 views. the method has been tested on a relatively large number of faces. In order to validate our system of panoramic face mosaicing, we also conducted a preliminary study on panoramic faces recognition, based on the principal component method. Experimental results show the feasibility and viability of our system.

关键词： Face recognition Cameras Face detection Humans Real time systems Costs Cognition Testing image processing pattern recognition

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Discrete representation of top points via scale space tessellation

Discrete representation of top points via scale space tessel...

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5th international Conference on Scale Space and PDE Methods in Computer Vision, Scale-Space 2005

作者： Platel, B. Demirci, M. Fatih Shokoufandeh, A. Florack, L.M.J. Kanters, F.M.W. Ter Haar Romeny, B.M. Dickinson, S.J. Eindhoven University of Technology P.O. Box 513 5600 MB Eindhoven Netherlands Drexel University 3141 Chestnut Street Philadelphia PA 19104 United States University of Toronto 6 King's College Rd. Toronto Ont. M5S 3G4 Canada

In previous work, singular points (or top points) in the scale space representation of generic images have proven valuable for image matching. In this paper, we propose a construction that encodes the scale space description of top points in the form of a directed acyclic graph. this representation allows us to utilize graph matching algorithms for comparing images represented in terms of top point configurations instead of using solely the top points and their features in a point matching algorithm, as was done previously. the nodes of the graph represent the critical paths together with their top points. the edge set will capture the neighborhood distribution of vertices in scale space, and is constructed through a Delaunay triangulation scheme. We also will present a many-to-many matching algorithm for comparing such graph-based representations. this algorithm is based on a metric-tree representation of labelled graphs and their low-distortion embeddings into normed vector spaces via spherical encoding. this is a two-step transformation that reduces the matching problem to that of computing a distribution-based distance measure between two such embeddings. To evaluate the quality of our representation, two sets of experiments are considered. First, the stability of this representation under Gaussian noise of increasing magnitude is examined. In the second set of experiments, a series of recognition experiments is run on a small face database. © Springer-Verlag Berlin Heidelberg 2005.

关键词： image processing

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Large-scale dataset cascading clustering by item set and space decomposition

Large-scale dataset cascading clustering by item set and spa...

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international Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH)

作者： Roman Melnyk Ruslan Tushnytskyy Software Department Computer Sciences and Information Technologies Institute Lviv Polytechnic National University Lviv Ukraine

In the paper two clustering approaches based on decomposition of item set and space are considered. Some investigation was held to compare the algorithm accuracy and complexity. the test results for some criteria func... 详细信息

关键词： Large-scale systems Clustering algorithms Testing Hypercubes pattern analysis pattern recognition Instruments image processing Micromechanical devices Process design

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Low-Light image Reconstruction Based On Improved Convolutional Autoencoder

Low-Light Image Reconstruction Based On Improved Convolution...

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Robotics & Intelligent Manufacturing Technology (ISRIMT), international symposium on

作者： Xuezhi Hu Xiaobin Xu College of Mechanical and Electrical Engineering Hohai University Changzhou China

Machine vision target detection can be mainly divided into traditional detection methods represented by Hof circle detection and template matching, and detection methods based on deep learning. Traditional target detection methods need to de-noise the image, binarization and other pre-processing methods to ensure that the contour of the target can be extracted, but in the actual operating environment, the interference generated by factors such as light and the surrounding environment often leads to the contour of the target in the image is difficult to be extracted, reducing the success rate of the traditional methods to extract the circle in the complex environment. And the deep learning based circle detection method can solve this problem. Deep learning methods can obtain more specific features, have a higher degree of recognition of the learned features, and have improved performance over traditional machine learning methods. therefore, we propose a convolutional autoencoder-based low-light image processing method, which improves the loss function by introducing the luminance module of SSIM in order to realize the adaptive enhancement of the luminance of low-light images.

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Examining How IP can be used to Find patterns for Industrial Automation

Examining How IP can be used to Find Patterns for Industrial...

引用

international Conference on Contemporary Computing and Informatics (IC3I)

作者： Alka Kumari Pooja Sapra Department of Computer Science ARKA JAIN University Jharkhand India Department of IT PIET Parul University Gujarat India

Visual input and output require image processing. IP inspired the technology’s name. this platform supports imaging, signal processing, image enhancement, and voice signal processing. Recognizable photos and videos have a distinct object or attribute. CBIR finds patterns in this study. IP in industrial automation was another topic. Industrial photo processing makes high-speed recording flawless. Automatic inspection systems use industrial image processing. Camera trigger sensors and rails are industrial imaging accessories.

关键词： Rails Visualization Automation Sensor phenomena and characterization Inspection pattern recognition Recording

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A hypergraph-based image database clustering framework

A hypergraph-based image database clustering framework

引用

international symposium on I/V Communications and Mobile Network (ISVC)

作者： Aurélien Ducournau Soufiane Rital Alain Bretto ENISE-DIPI EA 3719 Saint Etienne France LTCI CNRS TSI Telecom ParisTech Paris France Centre National de la Recherche Scientifique Paris ÃŽle-de-France FR

this paper describes a new approach to image database clustering. the method requires no a priori information. It works free of context and previous knowledge: in a first stage, the image features are formed automatically, and modeled by a p-Nearest Neighbor Hypergraph (p-NNH) representation. then images are clustered to form categories using a multilevel p-NNH partitioning approach. the partitioning approach operates on Coarsening-Paritioning-UnCoarsening scheme (CPUC). Categories are visualized by displaying the most typical image(s) of the categories as thumbnails. the main benefit of the approach is that it deals with a large volume image database and with a representation structure (hypergraph) that is close to the human visual grouping system. To judge results, an evaluation scheme which is adequate for the task of categorization is proposed.

关键词： image databases Partitioning algorithms Clustering algorithms image color analysis Heuristic algorithms pattern recognition Visualization

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