2007 IEEE Conference on Computer Vision and Pattern Recognition最新文献_第9页

Solving Large Scale Binary Quadratic Problems: Spectral Methods vs. Semidefinite Programming 求解大规模二元二次问题:谱方法与半定规划

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383202

Carl Olsson, Anders P. Eriksson, Fredrik Kahl

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引用次数: 56

Practical Online Active Learning for Classification 实用的在线分类主动学习

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383437

C. Monteleoni, Matti Kääriäinen

引用次数: 35

Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition 不变特征层次的无监督学习及其在目标识别中的应用

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383157

Marc'Aurelio Ranzato, Fu Jie Huang, Y-Lan Boureau, Yann LeCun

引用次数: 1159

Region Classification with Markov Field Aspect Models 基于马尔可夫场方面模型的区域分类

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383098

J. Verbeek, B. Triggs

{"title":"Region Classification with Markov Field Aspect Models","authors":"J. Verbeek, B. Triggs","doi":"10.1109/CVPR.2007.383098","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383098","url":null,"abstract":"Considerable advances have been made in learning to recognize and localize visual object classes. Simple bag-of-feature approaches label each pixel or patch independently. More advanced models attempt to improve the coherence of the labellings by introducing some form of inter-patch coupling: traditional spatial models such as MRF's provide crisper local labellings by exploiting neighbourhood-level couplings, while aspect models such as PLSA and LDA use global relevance estimates (global mixing proportions for the classes appearing in the image) to shape the local choices. We point out that the two approaches are complementary, combining them to produce aspect-based spatial field models that outperform both approaches. We study two spatial models: one based on averaging over forests of minimal spanning trees linking neighboring image regions, the other on an efficient chain-based Expectation Propagation method for regular 8-neighbor Markov random fields. The models can be trained using either patch-level labels or image-level keywords. As input features they use factored observation models combining texture, color and position cues. Experimental results on the MSR Cambridge data sets show that combining spatial and aspect models significantly improves the region-level classification accuracy. In fact our models trained with image-level labels outperform PLSA trained with pixel-level ones.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123826353","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: 248

Efficient Belief Propagation for Vision Using Linear Constraint Nodes 基于线性约束节点的视觉信念高效传播

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383094

B. Potetz

引用次数: 93

Shadow Removal in Front Projection Environments Using Object Tracking 使用目标跟踪去除正面投影环境中的阴影

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383470

S. Audet, J. Cooperstock

引用次数: 35

Learning Kernel Expansions for Image Classification 学习核扩展图像分类

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383151

F. D. L. Torre, Oriol Vinyals

引用次数: 29

Learning to Detect A Salient Object 学习发现一个突出的物体

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/cvpr.2007.383047

Tie Liu, Zejian Yuan, Jian Sun, Jingdong Wang, N. Zheng, Xiaoou Tang, H. Shum

引用次数: 2578

Optimizing Distribution-based Matching by Random Subsampling 随机子抽样优化基于分布的匹配

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383183

A. Leung, S. Gong

引用次数: 16

The Effect of Pixel-Level Fusion on Object Tracking in Multi-Sensor Surveillance Video 像素级融合对多传感器监控视频目标跟踪的影响

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383433

N. Cvejic, S. G. Nikolov, H. Knowles, A. Loza, A. Achim, D. Bull, C. N. Canagarajah

引用次数: 54