2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA)最新文献

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Mining Mid-Level Features for Action Recognition Based on Effective Skeleton Representation 基于有效骨架表示的动作识别中级特征挖掘

2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2014-09-13 DOI: 10.1109/DICTA.2014.7008115

Pichao Wang, W. Li, P. Ogunbona, Zhimin Gao, Hanling Zhang

{"title":"Mining Mid-Level Features for Action Recognition Based on Effective Skeleton Representation","authors":"Pichao Wang, W. Li, P. Ogunbona, Zhimin Gao, Hanling Zhang","doi":"10.1109/DICTA.2014.7008115","DOIUrl":"https://doi.org/10.1109/DICTA.2014.7008115","url":null,"abstract":"Recently, mid-level features have shown promising performance in computer vision. Mid-level features learned by incorporating class-level information are potentially more discriminative than traditional low-level local features. In this paper, an effective method is proposed to extract mid-level features from Kinect skeletons for 3D human action recognition. Firstly, the orientations of limbs connected by two skeleton joints are computed and each orientation is encoded into one of the 27 states indicating the spatial relationship of the joints. Secondly, limbs are combined into parts and the limb's states are mapped into part states. Finally, frequent pattern mining is employed to mine the most frequent and relevant (discriminative, representative and non-redundant) states of parts in continuous several frames. These parts are referred to as Frequent Local Parts or FLPs. The FLPs allow us to build powerful bag-of-FLP-based action representation. This new representation yields state-of-the-art results on MSR DailyActivity3D and MSR ActionPairs3D.","PeriodicalId":146695,"journal":{"name":"2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"61 17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114369058","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: 43

Multiple Instance Learning for Breast Cancer Magnetic Resonance Imaging 乳腺癌磁共振成像的多实例学习

2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 1900-01-01 DOI: 10.1109/DICTA.2014.7008118

F. A. Maken, Y. Gal, D. McClymont, A. Bradley

{"title":"Multiple Instance Learning for Breast Cancer Magnetic Resonance Imaging","authors":"F. A. Maken, Y. Gal, D. McClymont, A. Bradley","doi":"10.1109/DICTA.2014.7008118","DOIUrl":"https://doi.org/10.1109/DICTA.2014.7008118","url":null,"abstract":"In this paper we evaluate the suitability of multiple instance learning (MIL) for the classification of T2 weighted magnetic resonance images (MRI) of the breast. Specifically, we compare the performance of citation-kNN against traditional kNN and a random forest (RF) classifier. We utilise both (generic) tile-based features and (domain specific) region-of-interest (ROI) based features We perform experiments on two datasets consisting of A) mass-like lesions and B) both mass-like and non-mass-like lesions. The performance of citation-kNN as both a diagnostic and screening tool is evaluated using the area under the receiver operating characteristics curve (AUC), estimated over 10-fold cross-validation. Results demonstrate that citation- kNN has equivalent performance to traditional kNN and RF. However, the tile-based approach used by citation-kNN does not require the domain specific ROI-based features typically used in breast MRI. This not only makes citation-kNN robust to inaccuracies in the delineation of suspicious lesions, but also makes it suitable for use as a screening tool, where the aim is to discriminate lesions from normal tissue.","PeriodicalId":146695,"journal":{"name":"2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114968542","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

Detection of Anomalous Crowd Behaviour Using Hyperspherical Clustering 使用超球面聚类检测异常人群行为

2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 1900-01-01 DOI: 10.1109/DICTA.2014.7008100

A. S. Rao, J. Gubbi, S. Rajasegarar, S. Marusic, M. Palaniswami

引用次数: 15

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