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检索条件"主题词=feature coding"

共 68 条记录，以下是51-60 订阅

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A MULTIMODAL LOSSLESS coding METHOD FOR SKELETONS IN VIDEOS

A MULTIMODAL LOSSLESS CODING METHOD FOR SKELETONS IN VIDEOS

引用

IEEE International Conference on Multimedia and Expo (IEEE ICME)

作者： He, Xiaoyi Liu, Mingzhou Lin, Weiyao Han, Xintong Zhu, Yanmin Lu, Hongtao Xiong, Hongkai Shanghai Jiao Tong Univ Dept Elect Engn Shanghai Peoples R China Malong Technol Shenzhen Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781538692141

Nowadays, skeleton information in videos plays an important role in human-centric video analysis but effective coding such massive skeleton information has never been addressed in previous work. In this paper, we make the first attempt to solve this problem by proposing a multimodal skeleton coding tool containing three different coding schemes, namely, spatial differential-coding scheme, motion-vector-based differential-coding scheme and inter prediction scheme, thus utilizing both spatial and temporal redundancy to losslessly compress skeleton data. More importantly, these schemes are switched properly for different types of skeletons in video frames, hence achieving further improvement of compression rate. Experimental results show that our approach leads to 74.4% and 54.7% size reduction on our surveillance sequences and overall test sequences respectively, which demonstrates the effectiveness of our skeleton coding tool.

关键词： feature coding skeleton coding

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A New Persian/Arabic Text Steganography Using "La" Word

A New Persian/Arabic Text Steganography Using "La" Word

引用

International Conference on Systems, Computing Sciences and Software Engineering

作者： Shirali-Shahreza, Mohammad Sharif Univ Technol Dept Comp Sci Tehran Iran

ISBN: (纸本)9781402087400

By expanding communication, in some cases there is a need for hidden communication. Steganography is one of the methods used for hidden exchange of information. Steganography is a method to hide the information under a cover media such as image or sound. In this paper a new method for Steganography in Persian and Arabic texts is presented. Here we use the special form of "La" word for hiding the data. This word is created by connecting "Lam" and "Alef" characters. For hiding bit 0 we use the normal form of word "La" ("(sic)") by inserting Arabic extension character between "Lam" and "Alef" characters. But for hiding bit I we use the special form of word "La" ("gamma") which has a unique code in Unicode standard texts. Its code is FEFB in Unicode hex notation. This method is not limited to electronic documents (E-documents) and also can be used on printed documents. This approach can be categorized under feature coding methods.

关键词： feature coding Information Security Text Steganography Persian/Arabic Text Unicode Standard

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A Joint Compression Scheme for Local Binary feature Descriptors and their Corresponding Bag-of-Words Representation

A Joint Compression Scheme for Local Binary Feature Descript...

引用

IEEE Visual Communications and Image Processing (VCIP)

作者： Van Opdenbosch, Dominik Oelsch, Martin Garcea, Adrian Steinbach, Eckehard Tech Univ Munich Chair Media Technol Munich Germany

ISBN: (纸本)9781538604625

For real-time computer vision tasks, binary feature descriptors are an efficient alternative to their real-valued counterparts. While providing comparable results for many applications, the computational complexity of extracting and processing binary descriptors is significantly lower. In many application scenarios, the local features are transmitted over a channel with limited capacity and processed at a more powerful central processing unit, which requires efficient compression and transmission approaches. In this paper, we present a compression scheme for local binary features, which jointly encodes the descriptors and their respective Bag-of-Words representation using a shared vocabulary between client and server. By sending the visual word index and the entropy-coded residual vector containing the differences between the visual word and the descriptor, we are able to reduce ORB features to 60.62 % of their uncompressed size.

关键词： Visual features binary descriptors Bag-of-Words ORB feature coding ATC

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MULTI-VIEW DISTRIBUTED coding AND SELECTION OF LOCAL BINARY featureS

MULTI-VIEW DISTRIBUTED CODING AND SELECTION OF LOCAL BINARY ...

引用

IEEE International Conference on Multimedia & Expo (ICME)

作者： Monteiro, Nuno Brites, Catarina Pereira, Fernando Ascenso, Joao Inst Super Tecn Inst Telecomunicacoes Lisbon Portugal

ISBN: (纸本)9781467372589

Recently, the latest advances in compact feature representation and feature learning have provided an efficient framework for several visual analysis tasks, such as object recognition. However, when multiple cameras with overlapping fields-of-view are employed, other visual analysis tasks such as depth estimation can be supported and object recognition accuracy can be improved. In this paper the problem of distributed visual analysis from multiple views of a scene is addressed, considering that computational power and bandwidth, at each camera sensor, are rather limited. More specifically, an efficient coding technique for local binary features is proposed which exploits the correlation at the decoder side between each descriptor and its quantized representation. Moreover, considering that descriptors representing the same visual feature across different views are well correlated, a technique to avoid the transmission of redundant descriptors from multiple views is proposed. At the decoder, the joint statistics of all descriptors from all views is used to drive the selection of the best descriptors to be transmitted by each sensing node. The proposed multi-view feature coding and selection techniques allow obtaining bitrate reductions up to 80%, with respect to the uncompressed descriptor rate, for a certain task accuracy.

关键词： feature selection feature coding distributed coding distributed visual analysis

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Robust Head Pose Estimation using Locality-constrained Sparse coding 8

Robust Head Pose Estimation using Locality-constrained Spars...

引用

8th International Conference on Machine Vision (ICMV)

作者： Kim, Hyunduk Lee, Sang-Heon Sohn, Myoung-Kyu DGIST Dept IoT & Robot Convergence Res Taegu South Korea

ISBN: (纸本)9781510601161

Sparse coding (SC) method has been shown to deliver successful result in a variety of computer vision applications. However, it does not consider the underlying structure of the data in the feature space. On the other hand, locality-constrained linear coding (LLC) utilizes locality constraint to project each input data into its local-coordinate system. Based on the recent success of LLC, we propose a novel locality-constrained sparse coding (LSC) method to overcome the limitation of the SC. In experiments, the proposed algorithms were applied to head pose estimation applications. Experimental results demonstrated that the LSC method is better than state-of-the-art methods.

关键词： feature coding head pose estimation image classification locality-constraint sparse coding

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PDAP-GAN: Generative Adversarial Network for Power Data Anonymization Protection 24

PDAP-GAN: Generative Adversarial Network for Power Data Anon...

引用

International Conference on Power Electronics and Artificial Intelligence (PEAI)

作者： Guo, Qian Shen, Wen State Grid Smart Grid Res Inst Nanjing Peoples R China

ISBN: (纸本)9798400716638

Aiming at the problem that in the current environment of power data usage, the increased openness, frequent flow and complex interaction objects of data lead to the prevalence of data leakage risk throughout the data life cycle, this study proposes a generative adversarial network model for power data anonymization protection. The model first parses the original JSON file and encodes it using different feature encoders to effectively handle different types of variables. Second, the generative adversarial network is improved to generate anonymized data through loss feedback, and privacy is protected during data generation by adding random noise. Compared with the existing methods, the model is able to generate anonymized data with high utility and strong similarity to the original data for the original data of mixed data types, and realize decoupling with the original data. Experiments demonstrate that the data synthesized by the model proposed in this paper has significantly reduced differences in machine learning utility and statistical similarity compared to the original data, thus it can be used to replace the original data for mining analysis and data sharing, and effectively realize the privacy protection of the original data.

关键词： Generative adversarial networks data anonymization privacy preservation Loss feedback feature coding power data protection

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Local Spatiotemporal coding and Sparse Representation based Human Action Recognition

Local Spatiotemporal Coding and Sparse Representation based ...

引用

3rd International Conference on Frontiers of Manufacturing Science and Measuring Technology (ICFMM 2013)

作者： Wang, Bin Liu, Yu Wang, Wei Xu, Wei Zhang, Maojun Natl Univ Def Technol Coll Informat Syst & Manage Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9783037858462

To handle with the limitation of bag-of-features (BoF) model which ignores spatial and temporal relationships of local features in human action recognition in video, a Local Spatiotemporal coding (LSC) is proposed. Rather than the exiting methods only uses the feature appearance information for coding, LSC encodes feature appearance and spatiotemporal positions information simultaneously with vector quantization (VQ). It can directly models the spatiotemporal relationships of local features in space time volume (STY). In implement, the local features are projected into sub-space-time-volume (sub-STY), and encoded with LSC. In addition a multi-level LSC is also provided. Then a group of sub-STV descriptors obtained from videos with multi-level LSC and Avg-pooling are used for action video classification. A sparse representation based classification method is adopted to classify action videos upon these sub-STV descriptors. The experimental results on KTH, Weizmann, and UCF sports datasets show that our method achieves better performance than the previous local spatiotemporal features based human action recognition methods.

关键词： pattern recognition human action recognition feature coding spare representation classification

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EB-Boost: Medium-Long Term Forecasting Method of Supplies of Shared E-Cars in Smart Scenic Spots 11

EB-Boost: Medium-Long Term Forecasting Method of Supplies of...

引用

IEEE International Congress on Cybermatics / IEEE Conferences on Internet of Things, Green Computing and Communications, Cyber, Physical and Social Computing, Smart Data, Blockchain, Computer and Information Technology

作者： Li, Hong Yang, Xiaosheng Xia, Yao Zheng, Lujie Wang, Tie Yang, Guoqing Lv, Pan Zhejiang Univ Comp Sci & Technol Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781538679753

At present, shared e-cars have become an important part of traffic in scenic spots. While shared e-cars bring convenience to tourists, they also exert a certain influence on the management of the traffic environment in scenic spots. Nowadays, the threshold for shared e-cars is low, and all business operators occupy the market share of the scenic spots and put the shared-cars on the market without plans. This behavior takes up the limited space, exacerbating the congestion of scenic space. Therefore, predicting the amount of shared e-cars to be supplied by a scenic spot in the near future accurately is of great significance for major business entities to do resource scheduling, reduce operating costs, and create an open and coordinated traffic environment in smart scenic spots. However, the traditional time-series prediction models such as AutoRegressive and Moving Average Model, Holt-Winters, and Long ShortTerm Memory can only be used for short-term rough predictions, and cannot be available under some special circumstances such as holidays or rush hours. In our work, we proposed EB-Boost, an ensemble learning method using feature coding based on target. The EB-Boost studied from historical data such as weather data, timestamp data and business data and established probability model and learned relationship between features and targets. We used the data of shared e-cars operated by Roboy Technology company to build a model, and compared the prediction results of EB-Boost with traditional time-series algorithms and neural network algorithms. Finally, we also discuss the robustness of the model and the predictive effect of shared e-cars in other scenic spots.

关键词： Time-series Probability estimation Ensemble learning feature coding

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Lossy Intermediate Deep Learning feature Compression and Evaluation 19

Lossy Intermediate Deep Learning Feature Compression and Eva...

引用

27th ACM International Conference on Multimedia (MM)

作者： Chen, Zhuo Fan, Kui Wang, Shiqi Duan, Ling-Yu Lin, Weisi Kot, Alex C. Nanyang Technol Univ Singapore Singapore Peking Univ Shenzhen Peoples R China City Univ Hong Kong Hong Kong Peoples R China Peking Univ Beijing Peoples R China

ISBN: (纸本)9781450368896

With the unprecedented success of deep learning in computer vision tasks, many cloud-based visual analysis applications are powered by deep learning models. However, the deep learning models are also characterized with high computational complexity and are task-specific, which may hinder the large-scale implementation of the conventional data communication paradigms. To enable a better balance among bandwidth usage, computational load and the generalization capability for cloud-end servers, we propose to compress and transmit intermediate deep learning features instead of visual signals and ultimately utilized features. The proposed strategy also provides a promising way for the standardization of deep feature coding. As the first attempt to this problem, we present a lossy compression framework and evaluation metrics for intermediate deep feature compression. Comprehensive experimental results show the effectiveness of our proposed methods and the feasibility of the proposed data transmission strategy. It is worth mentioning that the proposed compression framework and evaluation metrics have been adopted into the ongoing AVS (Audio Video coding Standard Workgroup) - Visual feature coding Standard.

关键词： deep learning data transmission feature coding

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IMPROVED CLUSTER CENTER ADAPTION FOR IMAGE CLASSIFICATION

IMPROVED CLUSTER CENTER ADAPTION FOR IMAGE CLASSIFICATION

引用

IEEE International Conference on Image Processing (ICIP)

作者： Zhen, Mingmin Wang, Wenmin Wang, Ronggang Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Lishui Rd 2199 Shenzhen 518055 Peoples R China

ISBN: (纸本)9781479983391

The feature coding algorithm, "Vector of Locally Aggregated Descriptors (VLAD)", can be used effectively for large scale object instance retrieval. Despite its effectiveness and excellent performance, the existence of ambiguous cluster centers can reduce the performance. Though an idea to this problem has been proposed, it is not practical in fact. In this paper, we analyze possible situations that cause effect on the results and propose a novel approach to improve the VLAD method. The proposed method mainly focuses on the similarity measure between each two images. For each two images, we adapt the original cluster center to VLAD vectors. As we illustrate, our method has promising results with small vocabulary size on both datasets of 15 Scenes and VOC2007.

关键词： Image classification feature coding Image retrieval Image representation

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