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2010 20th International Conference on Pattern Recognition最新文献

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A Study on Detecting Patterns in Twitter Intra-topic User and Message Clustering Twitter话题内用户与消息聚类的模式检测研究
2010 20th International Conference on Pattern Recognition Pub Date : 2010-08-23 DOI: 10.1109/ICPR.2010.765
M. Cheong, V. Lee
{"title":"A Study on Detecting Patterns in Twitter Intra-topic User and Message Clustering","authors":"M. Cheong, V. Lee","doi":"10.1109/ICPR.2010.765","DOIUrl":"https://doi.org/10.1109/ICPR.2010.765","url":null,"abstract":"Timely detection of hidden patterns is the key for the analysis and estimating of driving determinants for mission critical decision making. This study applies Cheong and Lee’s “context-aware” content analysis framework to extract latent properties from Twitter messages (tweets). In addition, we incorporate an unsupervised Self-organizing Feature Map (SOM) as a machine learning-based clustering tool that has not been investigated in the context of opinion mining and sentimental analysis using microblogging. Our experimental results reveal the detection of interesting patterns for topics of interest which are latent and cannot be easily detected from the observed tweets without the aid of machine learning tools.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125101124","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: 35
Interest Point Based Tracking 兴趣点跟踪
2010 20th International Conference on Pattern Recognition Pub Date : 2010-08-23 DOI: 10.1109/ICPR.2010.866
Werner Kloihofer, M. Kampel
{"title":"Interest Point Based Tracking","authors":"Werner Kloihofer, M. Kampel","doi":"10.1109/ICPR.2010.866","DOIUrl":"https://doi.org/10.1109/ICPR.2010.866","url":null,"abstract":"This paper deals with a novel method for object tracking. In the first step interest points are detected and feature descriptors around them are calculated. Sets of known points are created, allowing tracking based on point matching. The set representation is updated online at every tracking step. Our method uses one-shot learning with the first frame, so no offline and no supervised learning is required. Following an object recognition based approach there is no need for a background model or motion model, allowing tracking of abrupt motion and with non-stationary cameras. We compare our method to Mean Shift and Tracking via Online Boosting, showing the benefits of our approach.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123283083","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: 35
Automated Tracking of Vesicles in Phase Contrast Microscopy Images 相衬显微镜图像中囊泡的自动跟踪
2010 20th International Conference on Pattern Recognition Pub Date : 2010-08-23 DOI: 10.1109/ICPR.2010.617
P. Usenik, T. Vrtovec, F. Pernus, B. Likar
{"title":"Automated Tracking of Vesicles in Phase Contrast Microscopy Images","authors":"P. Usenik, T. Vrtovec, F. Pernus, B. Likar","doi":"10.1109/ICPR.2010.617","DOIUrl":"https://doi.org/10.1109/ICPR.2010.617","url":null,"abstract":"We propose an algorithm for automated tracking of the contours of phospholipid vesicles, which can be used to evaluate the power, magnitude and frequency distribution of vesicle contour movements induced by thermal fluctuations. The algorithm was tested on vesicles with different structure composition that were exposed to variable temperature. The results show that the proposed algorithm is fast, robust and reliable, and that the resulting description of vesicle contours enables straightforward spectral analysis of their fluctuations, which can be also used for the determination of other vesicle properties, e.g. the bending rigidity or spontaneous curvature.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125284392","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
Discriminating Intended Human Objects in Consumer Videos 消费者视频中人类目标的区分
2010 20th International Conference on Pattern Recognition Pub Date : 2010-08-23 DOI: 10.1109/ICPR.2010.1065
Hiroshi Uegaki, Yuta Nakashima, N. Babaguchi
{"title":"Discriminating Intended Human Objects in Consumer Videos","authors":"Hiroshi Uegaki, Yuta Nakashima, N. Babaguchi","doi":"10.1109/ICPR.2010.1065","DOIUrl":"https://doi.org/10.1109/ICPR.2010.1065","url":null,"abstract":"In a consumer video, there are not only intended objects, which are intentionally captured by the camcorder user, but also unintended objects, which are accidentally framed-in. Since the intended objects are essential to present what the camcorder user wants to express in the video, discriminating the intended objects from the unintended objects are beneficial for many applications, e.g., video summarization, privacy protection, and so forth. In this paper, focusing on human objects, we propose a method for discriminating the intended human objects from the unintended human objects. We evaluated the proposed method using 10 videos captured by 3 camcorder users. The results demonstrate that the proposed method successfully discriminates the intended human objects with 0.45 of recall and 0.80 of precision.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125304128","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: 0
Information Fusion for Combining Visual and Textual Image Retrieval 视觉与文本图像检索相结合的信息融合
2010 20th International Conference on Pattern Recognition Pub Date : 2010-08-23 DOI: 10.1109/ICPR.2010.393
Xin Zhou, A. Depeursinge, H. Müller
{"title":"Information Fusion for Combining Visual and Textual Image Retrieval","authors":"Xin Zhou, A. Depeursinge, H. Müller","doi":"10.1109/ICPR.2010.393","DOIUrl":"https://doi.org/10.1109/ICPR.2010.393","url":null,"abstract":"In this paper, classical approaches such as maximum combinations (combMAX), sum combinations (comb-SUM) and the product of the maximum and a non–zero number (combMNZ) were employed and the trade–off between two fusion effects (chorus and dark horse effects) was studied based on the sum of n maximums. Various normalization strategies were tried out. The fusion algorithms are evaluated using the best four visual and textual runs of the ImageCLEF medical image retrieval task 2008 and 2009. The results show that fused runs outperform the best original runs and multi-modality fusion statistically outperforms single modality fusion. The logarithmic rank penalization shows to be the most stable normalization. The dark horse effect is in competition with the chorus effect and each of them can produce best fusion performance depending on the nature of the input data.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125431160","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: 44
A Generalized Anisotropic Diffusion for Defect Detection in Low-Contrast Surfaces 低对比度表面缺陷检测的广义各向异性扩散
2010 20th International Conference on Pattern Recognition Pub Date : 2010-08-23 DOI: 10.1109/ICPR.2010.1071
Shin-Min Chao, D. Tsai, Wei-Chen Li, Wei-Yao Chiu
{"title":"A Generalized Anisotropic Diffusion for Defect Detection in Low-Contrast Surfaces","authors":"Shin-Min Chao, D. Tsai, Wei-Chen Li, Wei-Yao Chiu","doi":"10.1109/ICPR.2010.1071","DOIUrl":"https://doi.org/10.1109/ICPR.2010.1071","url":null,"abstract":"In this paper, an anisotropic diffusion model with a generalized diffusion coefficient function is presented for defect detection in low-contrast surface images and, especially, aims at material surfaces found in liquid crystal display (LCD) manufacturing. A defect embedded in a low-contrast surface image is extremely difficult to detect because the intensity difference between unevenly-illuminated background and defective regions are hardly observable. The proposed anisotropic diffusion model provides a generalized diffusion mechanism that can flexibly change the curve of the diffusion coefficient function. It adaptively carries out a smoothing process for faultless areas and performs a sharpening process for defect areas in an image. An entropy criterion is proposed as the performance measure of the diffused image and then a stochastic evolutionary computation algorithm, particle swarm optimization (PSO), is applied to automatically determine the best parameter values of the generalized diffusion coefficient function. Experimental results have shown that the proposed method can effectively and efficiently detect small defects in low-contrast surface images.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115206778","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: 3
Document Logo Detection and Recognition Using Bayesian Model 基于贝叶斯模型的文档标识检测与识别
2010 20th International Conference on Pattern Recognition Pub Date : 2010-08-23 DOI: 10.1109/ICPR.2010.483
Hongye Wang
{"title":"Document Logo Detection and Recognition Using Bayesian Model","authors":"Hongye Wang","doi":"10.1109/ICPR.2010.483","DOIUrl":"https://doi.org/10.1109/ICPR.2010.483","url":null,"abstract":"This paper presents a simple, dynamic approach to logo detection and recognition in document images. Although there are literatures on both logo detection and logo recognition issues, Current methods lack the adaptability to variable real-world documents. In this paper we initially observe this deficiency from a different point of view and reveal its inherent causation. Then we reorganize the structure of the logo detection and recognition procedures and integrate them into a unified framework. By applying feedback and selecting proper features, we make our framework dynamic and interactive. Experiments show that the proposed method outperforms existing methods in document processing domain.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115335175","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: 22
Imbalance and Concentration in k-NN Classification k-NN分类中的不平衡与集中
2010 20th International Conference on Pattern Recognition Pub Date : 2010-08-23 DOI: 10.1109/ICPR.2010.531
Dawei Yin, Chang An, H. Baird
{"title":"Imbalance and Concentration in k-NN Classification","authors":"Dawei Yin, Chang An, H. Baird","doi":"10.1109/ICPR.2010.531","DOIUrl":"https://doi.org/10.1109/ICPR.2010.531","url":null,"abstract":"We propose algorithms for ameliorating difficulties in fast approximate k Nearest Neighbors (kNN) classifiers that arise from imbalances among classes in numbers of samples, and from concentrations of samples in small regions of feature space. These problems can occur with a wide range of binning kNN algorithms such as k-D trees and our variant, hashed k-D trees. The principal method we discuss automatically rebalances training data and estimates concentration in each K-d hash bin separately, which then controls how many samples should be kept in each bin. We report an experiment on 86.7M training samples which shows a 7-times speedup and higher minimum per-class recall, compared to previously reported methods. The context of these experiments is the need for image classifiers able to handle an unbounded variety of inputs: in our case, highly versatile document classifiers which require training sets as large as a billion training samples.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":"416 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115592501","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: 4
Sketched Symbol Recognition with a Latent-Dynamic Conditional Model 基于潜在动态条件模型的速写符号识别
2010 20th International Conference on Pattern Recognition Pub Date : 2010-08-23 DOI: 10.1109/ICPR.2010.275
V. Deufemia, M. Risi, G. Tortora
{"title":"Sketched Symbol Recognition with a Latent-Dynamic Conditional Model","authors":"V. Deufemia, M. Risi, G. Tortora","doi":"10.1109/ICPR.2010.275","DOIUrl":"https://doi.org/10.1109/ICPR.2010.275","url":null,"abstract":"In this paper we present a recognizer of sketched symbols based on Latent-Dynamic Conditional Random Fields (LDCRF), a discriminative model for sequence classification. The LDCRF model classifies unsegmented sequences of strokes into domain symbols by taking into account contextual and temporal information. In particular, LDCRFs learn the extrinsic dynamics among strokes by modeling a continuous stream of symbol labels, and learn internal stroke sub-structure by using intermediate hidden states. The performance of our work is evaluated in the electric circuit domain.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116075676","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
Object Recognition and Localization Via Spatial Instance Embedding 基于空间实例嵌入的目标识别与定位
2010 20th International Conference on Pattern Recognition Pub Date : 2010-08-23 DOI: 10.1109/ICPR.2010.119
Nazli Ikizler-Cinbis, S. Sclaroff
{"title":"Object Recognition and Localization Via Spatial Instance Embedding","authors":"Nazli Ikizler-Cinbis, S. Sclaroff","doi":"10.1109/ICPR.2010.119","DOIUrl":"https://doi.org/10.1109/ICPR.2010.119","url":null,"abstract":"We propose an approach for improving object recognition and localization using spatial kernels together with instance embedding. Our approach treats each image as a bag of instances (image features) within a multiple instance learning framework, where the relative locations of the instances are considered as well as the appearance similarity of the localized image features. The introduced spatial kernel augments the recognition power of the instance embedding in an intuitive and effective way, providing increased localization performance. We test our approach over two object datasets and present promising results.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116525069","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: 4
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