Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)最新文献_第3页

A new GPR image de-nosing method based on BEMD 一种基于BEMD的探地雷达图像去噪方法

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982709

Lu Gan, Long Zhou, Xinge You

引用次数: 5

Pornographic image classification based on top down color-saliency based BoW representation 基于自顶向下颜色显著性BoW表示的色情图像分类

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982698

Chunna Tian, Xiangnan Zhang, Xinbo Gao, Wei Wei

{"title":"Pornographic image classification based on top down color-saliency based BoW representation","authors":"Chunna Tian, Xiangnan Zhang, Xinbo Gao, Wei Wei","doi":"10.1109/SPAC.2014.6982698","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982698","url":null,"abstract":"Since color is an important visual clue of the pornographic image, this study presents a new framework for pornographic image classification based on the fusion of color and shape information for the bag of words representation. This framework contains three fusion patterns: The early fusion, late fusion and top down color-saliency based fusion, which are compared intensively. Based on the comparison, the top down color-saliency fusion based pornographic image classification method is proposed by using the statistical class prior of each color word to weight the shape word. In the late fusion and color-saliency based fusion, color name is adopt to represent the color information. To verify the effectiveness of spatial constrain on the words, we also compared the shape features quantized by vector quantization and locality-constrained linear coding. The experimental results show that our model combines the shape and color information properly and it is superior over the popular methods to distinguish the normal and pornographic-like images from the pornographic ones.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115576170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

The robust patches-based tracking method via sparse representation 基于稀疏表示的鲁棒补丁跟踪方法

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982667

Yi Li, Zhenyu He, Shuangyan Yi, Wei-Guo Yang

引用次数: 0

A knowledge acquisition model in maritime domain based on ontology 基于本体的海事领域知识获取模型

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982718

Dong Xia Zheng, Xue Da Sun

引用次数: 0

Privacy preserving for human object in video surveillance via visual cryptography 视频监控中人的隐私保护

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982661

Ling Du, Yuhang Li

{"title":"Privacy preserving for human object in video surveillance via visual cryptography","authors":"Ling Du, Yuhang Li","doi":"10.1109/SPAC.2014.6982661","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982661","url":null,"abstract":"This paper proposes a privacy preserving scheme for data security in the video surveillance. We firstly separate the foreground for each video frame, and obscure the separated human object by motion blur. For secure storage, each blurred foreground object is encrypted into N shares by visual cryptography, and stored into different servers. Each share is fully confidential and does not convey any meaningful information about the original video, so that breaking into one storage server do not induce any compromise. For legal requirement, the authorized users can recover the original content with better quality by non-blind deblurring algorithm. Moreover, thanks to our exploited foreground based encoding scheme, the data expansion introduced by distributed storage is greatly reduced. It is impossible for unauthorized users to recover the original content by the following reasons: 1) distributed video stream storage; 2) unknown blurring kernel; 3) inaccurate foreground content and mask. The performance evaluation on several surveillance scenarios demonstrates that our proposed method can effectively protect sensitive privacy information in surveillance videos.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132115016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Class specific dictionary learning for face recognition 类特定的字典学习人脸识别

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982690

Baodi Liu, Bin Shen, Yu-Xiong Wang

{"title":"Class specific dictionary learning for face recognition","authors":"Baodi Liu, Bin Shen, Yu-Xiong Wang","doi":"10.1109/SPAC.2014.6982690","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982690","url":null,"abstract":"Recently, sparse representation based classification (SRC) has been successfully used for visual recognition and showed impressive performance. Given a testing sample, SRC computes its sparse linear representation with respect to all the training samples and calculates the residual error for each class of training samples. However, SRC considers the training samples in each class contributing equally to the dictionary in that class, i.e., the dictionary consists of the training samples in that class. This may lead to high residual error and instability. In this paper, a class specific dictionary learning algorithm is proposed. First, by introducing the dual form of dictionary learning, an explicit relationship between the bases vectors and the original image features is represented, which enhances the interpretability. SRC can be thus considered to be a special case of the proposed algorithm. Second, blockwise coordinate descent algorithm and Lagrange multipliers are then applied to optimize the corresponding objective function. Extensive experimental results on three benchmark face recognition datasets demonstrate that the proposed algorithm has achieved superior performance compared with conventional classification algorithms.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114397213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Adaptive structured sub-blocks tracking 自适应结构化子块跟踪

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982651

Liu Jing-Wen, Sun Wei-Ping, Xia Tao

引用次数: 0

Robust inverse perspective mapping based on vanishing point 基于消失点的鲁棒反透视映射

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982733

Daiming Zhang, Bin Fang, Weibin Yang, Xiaosong Luo, Yuanyan Tang

引用次数: 25

STFT-like time frequency representations for nonstationary signal — From evenly sampled data to arbitrary nonuniformly sampled data 非平稳信号的类stft时频表示。从均匀采样数据到任意非均匀采样数据

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982725

Shujian Yu, Xinge You, Kexin Zhao, Xiubao Jiang, Yi Mou, Jie Zhu

{"title":"STFT-like time frequency representations for nonstationary signal — From evenly sampled data to arbitrary nonuniformly sampled data","authors":"Shujian Yu, Xinge You, Kexin Zhao, Xiubao Jiang, Yi Mou, Jie Zhu","doi":"10.1109/SPAC.2014.6982725","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982725","url":null,"abstract":"Spectrograms provide an effective way for time-frequency representation (TFR). Among these, short-time Fourier transform (STFT) based spectrograms are extensively used for various applications. However, STFT spectrogram and its revised versions suffer from two main issues: (1) there is a trade-off between time resolution and frequency resolution, and (2) almost all existing TFR methods, including STFT spectrogram, are not suitable to deal with nonuniformly sampled data. In this paper, we address these two problems by presenting alternative approaches, namely short-time amplitude and phase estimation (ST-APES) and short-time sparse learning via iterative minimization (ST-SLIM), to improve the resolution of STFT based spectrogram, and extend the applicability of our approaches to signals with arbitrary sampling patterns. Apart from evenly sampled data, we will consider missing data as well as arbitrary nonuniformly sampled data, at the same time. We will demonstrate via simulation results the superiority of our proposed algorithms in terms of resolution, sidelobe suppression and applicability to signals with arbitrary sampling patterns.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132749659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Fast mode selection algorithm based on derived layer 基于衍生层的快速模式选择算法

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982678

Yingyi Liang, Zhenyu He, Yi Li

引用次数: 0