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DAC-RAN: A DATA-ASSISTED CLOUD RADIO ACCESS NETWORK FOR visual COMMUNICATIONS

IEEE WIRELESS COMMUNICATIONS 2015年第3期22卷 130-136页

作者： Wu, Jun Liu, Dian Huang, Xin-Lin Luo, Chong Cui, Hao Wu, Feng Tongji Univ Comp Sci & Technol Dept Shanghai 200092 Peoples R China Univ Sci & Technol China Hefei Anhui Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 200092 Peoples R China Tongji Univ Dept Informat & Commun Engn Shanghai 200092 Peoples R China

The emergence of visual big data is a double-edged sword to mobile communications. On one hand, the massive scale of visual data transmission brings a huge challenge to the RAN. On the other, the abundant information in visual big data may improve the spectrum efficiency and robustness of visual communications. In this article, we propose a DaC-RAN architecture for visual communications, which integrates the ideas of SDN and C-RAN. We propose to separate the control and data planes in the conventional infrastructure, and integrate a new data plane specifically designed for visual communications into the virtual base station. We demonstrate the proposed DaC-RAN architecture through a practical visual communication system based on pseudo-analog transmission and CS-based data sampling and reconstruction. The correlation information retrieved from visual big data is utilized as prior knowledge in CS decoding. Preliminary simulation evaluations show that significant gain is achieved in spectrum efficiency over the conventional method.

关键词： Cloud computing Image reconstruction Big data Receivers Radio access networks Bayes methods visual databases

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Semi-supervised uncorrelated dictionary learning for colour face recognition

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IET COMPUTER VISION 2020年第3期14卷 92-100页

作者： Liu, Qian Jiang, Bo Zhang, Jia-lei Gao, Peng Xia, Zhi-jian Nanjing Univ Informat Sci & Technol Jiangsu Key Lab Meteorol Observat & Informat Proc Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Peoples R China

Colour images are increasingly used in the fields of computer vision, pattern recognition and machine learning, since they can provide more identifiable information than greyscale images. Thus, colour face recognition has attracted accumulating attention. Its key problem is how to remove the similarity between colour component images and take full advantage of colour difference information. Decision-level similarity reduction between colour component images directly affects the recognition effect, but it has been found in no work. In this study, the authors propose a novel colour face recognition approach named semi-supervised uncorrelated dictionary learning (SUDL), which realises decision-level similarity reduction and fusion of all colour components in face images. SUDL employs the labelled and unlabelled colour face image samples into structured dictionary learning to achieve three uncorrelated discriminating dictionaries corresponding to three colour components of face images, and then uses these dictionaries and the sparse coding technique to make a classification decision. Experimental results in multiple public colour face image databases demonstrate that the dictionary decorrelation, structured dictionary learning and unlabelled samples used in the proposed approach are effective and reasonable, and the proposed approach outperforms several representative colour face recognition methods in recognition rates, despite of its poor time performance.

关键词： face recognition learning (artificial intelligence) image colour analysis computer vision image recognition feature extraction visual databases pattern classification semisupervised uncorrelated dictionary learning colour images pattern recognition machine learning greyscale images colour component images colour difference information decision-level similarity reduction recognition effect colour face recognition approach colour components face images labelled colour face image samples unlabelled colour face image samples uncorrelated discriminating dictionaries multiple public colour face image databases dictionary decorrelation structured dictionary learning representative colour face recognition methods recognition rates

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Is 2D Unlabeled Data Adequate for Recognizing Facial Expressions?

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IEEE INTELLIGENT SYSTEMS 2016年第3期31卷 19-29页

作者： Tao, Lili Matuszewski, Bogdan J. Univ Cent Lancashire Preston PR1 2HE Lancs England Univ Bristol Visual Informat Lab Bristol BS8 1TH Avon England Univ Bristol Dept Comp Sci Bristol BS8 1TH Avon England Univ Cent Lancashire Comp Vis Coll Sci & Technol Preston PR1 2HE Lancs England

Automatic facial expression recognition is one of the important challenges for computer vision and machine learning. Despite the fact that many successes have been achieved in recent years, several important but unresolved problems remain. This article describes a facial expression recognition system based on the random forest technique. Contrary to the many previous methods, the proposed system uses only simple landmark features, with the view of possible real-time implementation on low-cost portable devices. Both supervised and unsupervised variants of the method are presented. However, the authors" main objective is to provide some quantitative experimental evidence behind more fundamental questions in facial articulation analysis, namely, the relative significance of 3D information as opposed to 2D data only and the importance of labeled training data in supervised learning as opposed to unsupervised learning. The comprehensive experiments were performed using the BU-3DFE facial expression database. These experiments not only show the effectiveness of the described methods but also demonstrate that the common assumptions about facial expression recognition are debatable.

关键词： computer vision emotion recognition face recognition learning (artificial intelligence) visual databases 2D unlabeled data BU-3DFE facial expression database automatic facial expression recognition computer vision facial articulation analysis machine learning random forest technique Face recognition Feature extraction Manifolds Supervised learning Three-dimensional displays Training data facial expression recognition intelligent systems nonlinear manifold learning random forests supervised and unsupervised learning

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DIEL: Interactive visualization Beyond the Here and Now

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2022年第1期28卷 737-746页

作者： Wu, Yifan Chang, Remco Hellerstein, Joseph M. Satyanarayan, Arvind Wu, Eugene Univ Calif Berkeley Berkeley CA 94720 USA Tufts Univ Medford MA 02155 USA MIT CSAIL Cambridge MA 02139 USA Columbia Univ New York NY 10027 USA

Interactive visualization design and research have primarily focused on local data and synchronous events. However, for more complex use cases-e.g., remote database access and streaming data sources-developers must grapple with distributed data and asynchronous events. Currently, constructing these use cases is difficult and time-consuming;developers are forced to operationally program low-level details like asynchronous database querying and reactive event handling. This approach is in stark contrast to modern methods for browser-based interactive visualization, which feature high-level declarative specifications. In response, we present DIEL, a declarative framework that supports asynchronous events over distributed data. As in many declarative languages, DIEL developers specify only what data they want, rather than procedural steps for how to assemble it. Uniquely, DIEL models asynchronous events (e.g., user interactions, server responses) as streams of data that are captured in event logs. To specify the state of a visualization at any time, developers write declarative queries over the data and event logs;DIEL compiles and optimizes a corresponding dataflow graph, and automatically generates necessary low-level distributed systems details. We demonstrate DIEL'S performance and expressivity through example interactive visualizations that make diverse use of remote data and asynchronous events. We further evaluate DIEL'S usability using the Cognitive Dimensions of Notations framework, revealing wins such as ease of change, and compromises such as premature commitments.

关键词： Data visualization Distributed databases databases Programming Libraries visual databases Servers Interactive visualization Toolkit Library Scalability Asynchrony

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Spontaneous facial expression database for academic emotion inference in online learning

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IET COMPUTER VISION 2019年第3期13卷 329-337页

作者： Bian, Cunling Zhang, Ya Yang, Fei Bi, Wei Lu, Weigang Ocean Univ China Dept Educ Technol Qingdao Shandong Peoples R China Shandong Univ Sch Comp Sci & Technol Jinan Shandong Peoples R China Shandong Univ Sch Mech Elect & Informat Engn Weihai Peoples R China SeeleTech Corp San Francisco CA USA Zsbatech Corp Beijing Peoples R China Ocean Univ China Dept Comp Sci & Technol Qingdao Shandong Peoples R China

Academic emotions can produce a great impact on the learning effect. Normally, emotions are expressed externally in the students' facial expressions, speech and behaviour. In this paper, the focus is on automatic academic emotion inference based on facial expressions in online learning. Considering the lack of training samples for the inference algorithm, a spontaneous facial expression database is established. It includes the facial expressions of five common academic emotions and consists of two subsets: a video clip database and an image database. A total of 1,274 video clips and 30,184 images from 82 students are included in the database. The samples are labelled by both the participants and external coders. An extensive analysis is carried out on the image database using a convolutional neural network (CNN)-based algorithm to infer self-annotation. Some data augmentation algorithms are applied to improve the algorithm performance. Additionally, an adaptive data augmentation algorithm based on spatial transformer network is introduced, which can remove some confounding factors in the original images. The algorithm can obviously improve the inference performance, which has been proven by comparing some evaluation indicators before and after adoption. Such a database will certainly accelerate the application of affective computing in the educational field.

关键词： learning (artificial intelligence) neural nets visual databases face recognition emotion recognition inference mechanisms computer aided instruction image classification coefficient external coders image database convolutional neural network-based algorithm data augmentation algorithms adaptive data augmentation algorithm inference performance spontaneous facial expression database online learning learning effect students automatic academic emotion inference facial expressions inference algorithm common academic emotions video clip database 1274 video clips

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Saliency field map construction for region-of-interest-based color image querying

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JOURNAL OF ELECTRONIC IMAGING 2005年第3期14卷 033012.1-033012.9页

作者： Celenk, M Zhou, QA Vetnes, V Godavari, R Ohio Univ Sch Elect Engn & Comp Sci Stocker Ctr Athens OH 45701 USA

Spectral (color) and spatial (shape) features available in pictures are two significant sources of information for content-based retrieval of image databases. The developed adaptive shape transform approach originated from the premise that a two-dimensional (2-D) shape can be recovered from a set of Radon-transtorm-based projections. For search consistency, it is necessary to identify the region(s) of interest (ROI) before applying the Radon transform to the shape query. ROIs are detected automatically by means of saliency-map-based segmentation. The Radon transform packs the shape information of a 2-D object along the projection axes of known orientation, and generates a series of one-dimensional (1-D) functions from color channels for projection angles ranging from 0 to 180 deg. The optimal number of projections for a particular shape is determined by imposing the Kullback-Leibler divergence (KLD) distance measure as the similarity metric between the query and database images. The Radon transforms with the shortest and longest lengths yield the most distinctive shape attributes for the object classes being queried and enable the feature space to be invariant to translation and rotation in the spatial plane. The proposed algorithm is tested on a wide range of color images with complex shaped objects and different spatial resolutions. The KLDs between two images are calculated in the longest and shortest directions of the Radon transform, and then are summed together to find the similarity measure between the query and database pictures. Experimental results are close to those that a human observer expects. Further, the method is quite robust and it can account for high image noise (Shao and Celenk, 2001). (c) 2005 SPIE and IS&T.

关键词： similar measure object class color image visual databases shape information Eigenvector Radon transforms Content retrieval

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visualizing a knowledge domain's intellectual structure

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COMPUTER 2001年第3期34卷 65-+页

作者： Chen, CM Paul, RJ Brunel Univ Dept Informat Syst & Comp Uxbridge UB8 3PH Middx England Brunel Univ VIVID Res Ctr Uxbridge UB8 3PH Middx England Brunel Univ Ctr Appl Simulat Modeling Uxbridge UB8 3PH Middx England Brunel Univ Dept Informat Syst & Comp Ctr Living Informat Syst Thinking Uxbridge UB8 3PH Middx England

To make knowledge visualizations clear and easy to interpret, we have developed a method that extends and transforms traditional author co citation analysis by extracting structural patterns from the scientific litera... 详细信息

关键词： Data visualization visual databases Intersymbol interference Information retrieval Data mining Indexes Graphics Logic programming Multidimensional systems Simulated annealing

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Multiresolution descriptor matching algorithm for fast exhaustive search in norm-sorted databases

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JOURNAL OF ELECTRONIC IMAGING 2005年第4期14卷 043019.1-043019.7页

作者： Song, BC Ra, JB Samsung Elect Co Ltd Digital Media Res & Dev Ctr Suwon 443742 South Korea Korea Adv Inst Sci & Technol Dept Elect Engn & Comp Sci Taejon 305701 South Korea

To determine the best match to a query image in a database, conventional content-based image retrieval schemes require the exhaustive search, where the descriptor of the query, e. g., the histogram, must be compared with literally all images in the database. However, the straightforward exhaustive search algorithm is computationally expensive. Thus, fast exhaustive search algorithms are required. We present a fast exhaustive search algorithm based on a multiresolution descriptor structure and a norm-sorted database. First, we derive a condition to eliminate unnecessary matching operations from the search procedure by using a norm-sorted structure of the database. Then, we propose a fast search algorithm based on the elimination condition, which guarantees an exhaustive search for either the best match or multiple best matches to a query. With a luminance histogram as a descriptor, we show that the proposed algorithm provides a search accuracy of 100% with a high search speed. (c) 2005 SPIE and IS&T.

关键词： Descriptors Fasting exhaustive search algorithms SEARCHING ALGORITHM Database visual databases Optimal matching histograms retrieval speed

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Exploratory visualization Involving Incremental, Approximate Database Queries and Uncertainty

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IEEE COMPUTER GRAPHICS AND APPLICATIONS 2012年第4期32卷 55-62页

作者： Fisher, Danyel Drucker, Steven M. Koenig, A. Christian Microsoft Research Microsoft XCG

Large datasets can mean slow queries, for which users must wait. Incremental visualization systems can give faster results at a cost of accuracy. This article asked analysts to use one and report on their results. The... 详细信息

关键词： box plots computer graphics Data visualization Games incremental visualization information visualization Large-scale systems Probability distribution probability distributions visual analytics visual databases

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GLIP: A Concurrency Control Protocol for Clipping Indexing

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2009年第5期21卷 714-728页

作者： Lu, Chang-Tien Dai, Jing Jin, Ying Mathuria, Janak Virginia Tech Dept Comp Sci Falls Church VA 22043 USA Virginia Tech Dept Comp Sci Blacksburg VA 24060 USA

Multidimensional databases are beginning to be used in a wide range of applications. To meet this fast-growing demand, the R-tree family is being applied to support fast access to multidimensional data, for which the R+-tree exhibits outstanding search performance. In order to support efficient concurrent access in multiuser environments, concurrency control mechanisms for multidimensional indexing have been proposed. However, these mechanisms cannot be directly applied to the R+-tree because an object in the R+-tree may be indexed in multiple leaves. This paper proposes a concurrency control protocol for R-tree variants with object clipping, namely, Granular Locking for clIPping indexing (GLIP). GLIP is the first concurrency control approach specifically designed for the R+-tree and its variants, and it supports efficient concurrent operations with serializable isolation, consistency, and deadlock-free. Experimental tests on both real and synthetic data sets validated the effectiveness and efficiency of the proposed concurrent access framework.

关键词： Access methods Concurrency Spatial databases Concurrency access method multi-dimensional database Concurrency control control protocol clipping Indexing Concurrent Access visual databases Line clipping

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