2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)最新文献_第9页

Hallucination space relationship learning to improve very low resolution face recognition 幻觉空间关系学习，提高非常低分辨率的人脸识别

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486455

Juhyun Ahn, Daijin Kim, S. I. Ch'ng

引用次数: 0

Fine pose estimation of known objects in cluttered scene images 混乱场景图像中已知物体的精细姿态估计

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486579

Sudipto Banerjee, Sanchit Aggarwal, A. Namboodiri

引用次数: 1

Efficient objectness via saliency seeds and contour segments 通过显著性种子和轮廓段有效地对象化

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486613

Rigen Te, Cheng Yan

引用次数: 0

Multi-attribute learning for pedestrian attribute recognition in surveillance scenarios 监控场景下行人属性识别的多属性学习

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486476

Dangwei Li, Xiaotang Chen, Kaiqi Huang

{"title":"Multi-attribute learning for pedestrian attribute recognition in surveillance scenarios","authors":"Dangwei Li, Xiaotang Chen, Kaiqi Huang","doi":"10.1109/ACPR.2015.7486476","DOIUrl":"https://doi.org/10.1109/ACPR.2015.7486476","url":null,"abstract":"In real video surveillance scenarios, visual pedestrian attributes, such as gender, backpack, clothes types, are very important for pedestrian retrieval and person reidentification. Existing methods for attributes recognition have two drawbacks: (a) handcrafted features (e.g. color histograms, local binary patterns) cannot cope well with the difficulty of real video surveillance scenarios; (b) the relationship among pedestrian attributes is ignored. To address the two drawbacks, we propose two deep learning based models to recognize pedestrian attributes. On the one hand, each attribute is treated as an independent component and the deep learning based single attribute recognition model (DeepSAR) is proposed to recognize each attribute one by one. On the other hand, to exploit the relationship among attributes, the deep learning framework which recognizes multiple attributes jointly (DeepMAR) is proposed. In the DeepMAR, one attribute can contribute to the representation of other attributes. For example, the gender of woman can contribute to the representation oflong hair and wearing skirt. Experiments on recent popular pedestrian attribute datasets illustrate that our proposed models achieve the state-of-the-art results.","PeriodicalId":240902,"journal":{"name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125307728","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: 220

1000fps human segmentation with deep convolutional neural networks 1000fps的深度卷积神经网络人体分割

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486548

Chunfeng Song, Yongzhen Huang, Zhenyu Wang, Liang Wang

引用次数: 19

Transfer forest based on covariate shift 基于协变量移位的转移林

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486605

M. Tsuchiya, Yuji Yamauchi, Takayoshi Yamashita, H. Fujiyoshi

引用次数: 8

A novel fuzzy LBP based symbolic representation technique for classification of medicinal plants 一种新的基于模糊LBP的药用植物分类符号表示技术

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486558

Y. Naresh, H. S. Nagendraswamy

{"title":"A novel fuzzy LBP based symbolic representation technique for classification of medicinal plants","authors":"Y. Naresh, H. S. Nagendraswamy","doi":"10.1109/ACPR.2015.7486558","DOIUrl":"https://doi.org/10.1109/ACPR.2015.7486558","url":null,"abstract":"In this paper, a novel fuzzy LBP model for extracting texture features from medicinal plant leaves is proposed. The proposed method is invariant to image transformations and independent of any threshold. Concept of hierarchical clustering based on inconsistency coefficient is used to produce natural clusters for a particular species capturing intra-class variations due to environmental conditions and acquisition system. Interval valued type symbolic feature vector is used to represent each cluster effectively. Thus the proposed system suggests choosing multiple representatives for each species to make the representation more effective and robust. A chi-square distance measure is used to establish matching between the test and reference feature vectors of plant leaves and a nearest neighbor classification technique is used to classify an unknown test sample of medicinal plant leaf. Extensive experiments are conducted to demonstrate the efficacy of the proposed model on our own data set and other publically available leaf datasets. Results of the proposed work has been compared with the contemporary work and found to be superior.","PeriodicalId":240902,"journal":{"name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128850027","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

Fine-grain annotation of cricket videos 板球视频的精细注释

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486538

R. Sharma, K. Sankar, C. V. Jawahar

{"title":"Fine-grain annotation of cricket videos","authors":"R. Sharma, K. Sankar, C. V. Jawahar","doi":"10.1109/ACPR.2015.7486538","DOIUrl":"https://doi.org/10.1109/ACPR.2015.7486538","url":null,"abstract":"The recognition of human activities is one of the key problems in video understanding. Action recognition is challenging even for specific categories of videos, such as sports, that contain only a small set of actions. Interestingly, sports videos are accompanied by detailed commentaries available online, which could be used to perform action annotation in a weakly-supervised setting. For the specific case of Cricket videos, we address the challenge of temporal segmentation and annotation of actions with semantic descriptions. Our solution consists of two stages. In the first stage, the video is segmented into \"scenes\", by utilizing the scene category information extracted from text-commentary. The second stage consists of classifying videoshots as well as the phrases in the textual description into various categories. The relevant phrases are then suitably mapped to the video-shots. The novel aspect of this work is the fine temporal scale at which semantic information is assigned to the video. As a result of our approach, we enable retrieval of specific actions that last only a few seconds, from several hours of video. This solution yields a large number of labelled exemplars, with no manual effort, that could be used by machine learning algorithms to learn complex actions.","PeriodicalId":240902,"journal":{"name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121708556","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: 18

Float greedy-search-based subspace clustering 浮动基于贪婪搜索的子空间聚类

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486494

Lingxiao Song, Man Zhang, Qi Li, Zhenan Sun, R. He

引用次数: 0

CRF with locality-consistent dictionary learning for semantic segmentation 基于位置一致字典学习的语义分割CRF

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486555

Yi Li, Yanqing Guo, J. Guo, Ming Li, Xiangwei Kong

引用次数: 5