2013 IEEE Conference on Computer Vision and Pattern Recognition最新文献_第9页

Optimized Product Quantization for Approximate Nearest Neighbor Search 近似最近邻搜索的优化产品量化

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.379

T. Ge, Kaiming He, Qifa Ke, Jian Sun

引用次数: 367

Submodular Salient Region Detection 子模显著区检测

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.266

Zhuolin Jiang, L. Davis

引用次数: 154

Complex Event Detection via Multi-source Video Attributes 基于多源视频属性的复杂事件检测

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.339

Zhigang Ma, Yi Yang, Zhongwen Xu, Shuicheng Yan, N. Sebe, Alexander Hauptmann

{"title":"Complex Event Detection via Multi-source Video Attributes","authors":"Zhigang Ma, Yi Yang, Zhongwen Xu, Shuicheng Yan, N. Sebe, Alexander Hauptmann","doi":"10.1109/CVPR.2013.339","DOIUrl":"https://doi.org/10.1109/CVPR.2013.339","url":null,"abstract":"Complex events essentially include human, scenes, objects and actions that can be summarized by visual attributes, so leveraging relevant attributes properly could be helpful for event detection. Many works have exploited attributes at image level for various applications. However, attributes at image level are possibly insufficient for complex event detection in videos due to their limited capability in characterizing the dynamic properties of video data. Hence, we propose to leverage attributes at video level (named as video attributes in this work), i.e., the semantic labels of external videos are used as attributes. Compared to complex event videos, these external videos contain simple contents such as objects, scenes and actions which are the basic elements of complex events. Specifically, building upon a correlation vector which correlates the attributes and the complex event, we incorporate video attributes latently as extra informative cues into the event detector learnt from complex event videos. Extensive experiments on a real-world large-scale dataset validate the efficacy of the proposed approach.","PeriodicalId":6343,"journal":{"name":"2013 IEEE Conference on Computer Vision and Pattern Recognition","volume":"27 1","pages":"2627-2633"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81301777","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: 73

Detection Evolution with Multi-order Contextual Co-occurrence 多阶上下文共现的检测进化

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.235

Guang Chen, Yuanyuan Ding, Jing Xiao, T. Han

{"title":"Detection Evolution with Multi-order Contextual Co-occurrence","authors":"Guang Chen, Yuanyuan Ding, Jing Xiao, T. Han","doi":"10.1109/CVPR.2013.235","DOIUrl":"https://doi.org/10.1109/CVPR.2013.235","url":null,"abstract":"Context has been playing an increasingly important role to improve the object detection performance. In this paper we propose an effective representation, Multi-Order Contextual co-Occurrence (MOCO), to implicitly model the high level context using solely detection responses from a baseline object detector. The so-called (1st-order) context feature is computed as a set of randomized binary comparisons on the response map of the baseline object detector. The statistics of the 1st-order binary context features are further calculated to construct a high order co-occurrence descriptor. Combining the MOCO feature with the original image feature, we can evolve the baseline object detector to a stronger context aware detector. With the updated detector, we can continue the evolution till the contextual improvements saturate. Using the successful deformable-part-model detector [13] as the baseline detector, we test the proposed MOCO evolution framework on the PASCAL VOC 2007 dataset [8] and Caltech pedestrian dataset [7]: The proposed MOCO detector outperforms all known state-of-the-art approaches, contextually boosting deformable part models (ver. 5) [13] by 3.3% in mean average precision on the PASCAL 2007 dataset. For the Caltech pedestrian dataset, our method further reduces the log-average miss rate from 48% to 46% and the miss rate at 1 FPPI from 25% to 23%, compared with the best prior art [6].","PeriodicalId":6343,"journal":{"name":"2013 IEEE Conference on Computer Vision and Pattern Recognition","volume":"4 1","pages":"1798-1805"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81398816","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: 93

Supervised Semantic Gradient Extraction Using Linear-Time Optimization 基于线性时间优化的监督语义梯度提取

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.364

Shulin Yang, Jue Wang, L. Shapiro

引用次数: 2

Learning Binary Codes for High-Dimensional Data Using Bilinear Projections 使用双线性投影学习高维数据的二进制代码

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.69

Yunchao Gong, Sanjiv Kumar, H. Rowley, S. Lazebnik

引用次数: 185

Robust Region Grouping via Internal Patch Statistics 基于内部补丁统计的鲁棒区域分组

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.252

Xiaobai Liu, Liang Lin, A. Yuille

引用次数: 13

Fine-Grained Crowdsourcing for Fine-Grained Recognition 细粒度众包实现细粒度识别

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.81

Jia Deng, J. Krause, Li Fei-Fei

引用次数: 300

Nonparametric Scene Parsing with Adaptive Feature Relevance and Semantic Context 基于自适应特征关联和语义上下文的非参数场景解析

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.405

Gautam Singh, J. Kosecka

引用次数: 97

Adherent Raindrop Detection and Removal in Video 视频中附着雨滴的检测和去除

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.138

Shaodi You, R. Tan, Rei Kawakami, K. Ikeuchi

{"title":"Adherent Raindrop Detection and Removal in Video","authors":"Shaodi You, R. Tan, Rei Kawakami, K. Ikeuchi","doi":"10.1109/CVPR.2013.138","DOIUrl":"https://doi.org/10.1109/CVPR.2013.138","url":null,"abstract":"Raindrops adhered to a windscreen or window glass can significantly degrade the visibility of a scene. Detecting and removing raindrops will, therefore, benefit many computer vision applications, particularly outdoor surveillance systems and intelligent vehicle systems. In this paper, a method that automatically detects and removes adherent raindrops is introduced. The core idea is to exploit the local spatio-temporal derivatives of raindrops. First, it detects raindrops based on the motion and the intensity temporal derivatives of the input video. Second, relying on an analysis that some areas of a raindrop completely occludes the scene, yet the remaining areas occludes only partially, the method removes the two types of areas separately. For partially occluding areas, it restores them by retrieving as much as possible information of the scene, namely, by solving a blending function on the detected partially occluding areas using the temporal intensity change. For completely occluding areas, it recovers them by using a video completion technique. Experimental results using various real videos show the effectiveness of the proposed method.","PeriodicalId":6343,"journal":{"name":"2013 IEEE Conference on Computer Vision and Pattern Recognition","volume":"20 1","pages":"1035-1042"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85602857","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: 66