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Efficient Retrieval for Large Scale Metric Learning 1

引用

35th German conference on pattern recognition (GCPR)

作者： Koestinger, Martin Roth, Peter M. Bischof, Horst Graz Univ Technol Inst Comp Graph & Vision A-8010 Graz Austria

ISBN: (数字)9783642406027

ISBN: (纸本)9783642406010;9783642406027

In this paper, we address the problem of efficient k-NN classification. In particular, in the context of Mahalanobis metric learning. Mahalanobis metric learning recently demonstrated competitive results for a variety of tasks. However, such approaches have two main drawbacks. First, learning metrics requires often to solve complex and thus computationally very expensive optimization problems. Second, as the evaluation time linearly scales with the size of the data k-NN becomes cumbersome for large-scale problems or real-time applications with limited time budget. To overcome these problems, we propose a metric-based hashing strategy, allowing for both, efficient learning and evaluation. In particular, we adopt an efficient metric learning method for local sensitive hashing that recently demonstrated reasonable results for several large-scale benchmarks. In fact, if the intrinsic structure of the data is exploited by the metric in a meaningful way, using hashing we can compact the feature representation still obtaining competitive results. this leads to a drastically reduced evaluation effort. Results on a variety of challenging benchmarks with rather diverse nature demonstrate the power of our method. these include standard machine learning datasets as well as the challenging Public Figures Face Database. On the competitive machine learning benchmarks we obtain results comparable to the state-of-the-art Mahalanobis metric learning and hashing approaches. On the face benchmark we clearly outperform the state-of-the-art in Mahalanobis metric learning. In both cases, however, with drastically reduced evaluation effort.

关键词： Budget control

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Compressed sensing ensemble classifier for human detection

Compressed sensing ensemble classifier for human detection

引用

4th International conference on Intelligence Science and Big Data Engineering, IScIDE 2013

作者： Zhang, Baochang Liu, Juan Gao, Yongsheng Liu, Jianzhuang Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering BeiHang University Beijing 100191 China School of Engineering Griffith University Australia Shenzhen Key Lab for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Department of Information Engineering Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9783642420566

this paper proposes a novel Compressed Sensing Ensemble Classifier (CSEC) for human detection. the proposed CSEC employs the compressed sensing technique to get a more sparse model with a more reasonable selection of base classifiers. the major contributions of this paper are: 1) a novel principled framework for ensemble classifier design based on compressed sensing;2) a new concept of considering both the simplicity of ensemble classifier and irrelevance of base classifiers towards optimal classifier design;and 3) a quadratic function for CSEC optimization which includes a new optimizable positive semi-definite relevance matrix to simultaneously select appropriate base classifiers with minimized relevance. Experimental results on INRIA and SDL databases show that the performance of CSEC is better than two most popular classifiers SVM and AdaBoost, as well as a most recent method CLML. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Compressed sensing

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Multi-scale Block Local Ternary patterns for Fingerprints Vitality Detection

Multi-scale Block Local Ternary Patterns for Fingerprints Vi...

引用

6th IAPR International conference on Biometrics (ICB)

作者： Jia, Xiaofei Yang, Xin Zang, Yali Zhang, Ning Dai, Ruwei Tian, Jie Zhao, Jianmin Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Zhejiang Normal Univ Coll Math Phys & Informat Technol Zhejiang Peoples R China

ISBN: (纸本)9781479903108

Fingerprint recognition systems are widely deployed in both government and civilian applications. But the emergence of fake fingerprints poses a new threat to privacy security. Among the numerous fingerprint vitality detection methods, the local binary pattern (LBP) is considered as one of the state-of-the-art operators. However, the local binary pattern tends to be sensitive to noise, as there are no types of filters involved in the LBP operator. Worse still, the LBP operator can not reflect these difference whether the pixel value is bigger than the threshold or equal to the threshold. So we proposed a novel fingerprint vitality detection method based on multi-scale block local ternary patterns (MBLTP). Instead of a single pixel, its computation is done based on the average value of blocks. the ternary pattern is adopted to reflect the differences between the pixels and the threshold. the experimental results in the databases of Competition on Fingerprint Liveness Detection 2011 (LivDet 2011) show its superiority.

关键词： Local binary pattern

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Background subtraction based on multi-channel SILTP

Background subtraction based on multi-channel SILTP

引用

11th Asian conference on computer vision, ACCV 2012

作者： Ma, Fan Sang, Nong Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan Hubei 430074 China

ISBN: (纸本)9783642374098

Background subtraction is the first step in many video surveillance systems, its performance has a decisive influence on the result of the post-processing. An effective background subtraction algorithm should distinguish foreground from the background sensitively, and adapt to the variation of background scenes robustly, such as illumination changes or dynamic scenes. In this paper, a novel pixel-wise background subtraction algorithm is introduced. First, we propose a novel texture descriptor named Multi-Channel Scale Invariant Local Ternary pattern(MC-SILTP). the pattern is cross-calculated in RGB color channels with the Scale Invariant Local Ternary pattern operator. this descriptor does not only show an excellent performance in abundant texture regions, but also in flat regions. Secondly, we model each background pixel with a codebook rather than estimating the probability density functions. the codebook is consisted of many MC-SILTP samples actually observed in the past. A lot of experiments have been done over the proposed approach, results indicates that this approach is well balanced in sensitivity and robustness. It can handle the tricky problem of illumination changes robustly while detecting complete objects in flat areas sensitively. Comparison between the proposed one and several popular background subtraction algorithms demonstrates that it outperforms the state-of-the-art. © 2013 Springer-Verlag.

关键词： Probability density function

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Co-regularized PLSA for multi-view clustering

Co-regularized PLSA for multi-view clustering

引用

11th Asian conference on computer vision, ACCV 2012

作者： Jiang, Yu Liu, Jing Li, Zechao Li, Peng Lu, Hanqing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9783642374432

Multi-view data is common in a wide variety of application domains. Properly exploiting the relations among different views is helpful to alleviate the difficulty of a learning problem of interest. To this end, we propose an extended Probabilistic Latent Semantic Analysis (PLSA) model for multi-view clustering, named Co-regularized PLSA (CoPLSA). CoPLSA integrates individual PLSAs in different views by pairwise co-regularization. the central idea behind the co-regularization is that the sample similarities in the topic space from one view should agree with those from another view. An EM-based scheme is employed for parameter estimation, and a local optimal solution is obtained through an iterative process. Extensive experiments are conducted on three real-world datasets and the compared results demonstrate the superiority of our approach. © 2013 Springer-Verlag.

关键词： Semantics

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Contextual pooling in image classification

Contextual pooling in image classification

引用

11th Asian conference on computer vision, ACCV 2012

作者： Wu, Zifeng Huang, Yongzhen Wang, Liang Tan, Tieniu National Lab. of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9783642373305

the original bag-of-words (BoW) model in terms of image classification treats each local feature independently, and thus ignores the spatial relationships between a feature and its neighboring features, namely, the feature's context. However, our intuition and empirical studies tell the importance of such spatial information. Although the global spatial information can be captured with the spatial pyramid matching scheme, the subject of capturing local spatial relationships between features is still open. In this paper, we propose a new method to embed such local spatial (context) information into the BoW model. A vector reflecting context information is firstly extracted along with each feature, context patterns are then code-specifically trained, and thus the context information is elegantly embedded into the BoW model by contextual pooling according to different context patterns. Extensive experiments on the PASCAL VOC 2007 dataset show that our method greatly enhances the BoW model, and achieves the state-of-the-art performance. © 2013 Springer-Verlag.

关键词： Image classification

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Modeling hidden topics with dual local consistency for image analysis

Modeling hidden topics with dual local consistency for image...

引用

11th Asian conference on computer vision, ACCV 2012

作者： Li, Peng Cheng, Jian Lu, Hanqing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9783642373305

Image representation is the crucial component in image analysis and understanding. However, the widely used low-level features cannot correctly represent the high-level semantic content of images in many situations due to the "semantic gap". In order to bridge the "semantic gap", in this brief, we present a novel topic model, which can learn an effective and robust mid-level representation in the latent semantic space for image analysis. In our model, the 1-graph is constructed to model the local image neighborhood structure and the word co-occurrence is computed to capture the local word consistency. then, the local information is incorporated into the model for topic discovering. Finally, the generalized EM algorithm is used to estimate the parameters. As our model considers both the local image structure and local word consistency simultaneously when estimating the probabilistic topic distributions, the image representations can have more powerful description ability in the learned latent semantic space. Extensive experiments on the publicly available databases demonstrate the effectiveness of our approach. © 2013 Springer-Verlag.

关键词： Image analysis

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Measuring depth accuracy in RGBD cameras

Measuring depth accuracy in RGBD cameras

引用

International conference on Signal Processing and Communication Systems, ICSPCS

作者： Hussein Haggag Mohammed Hossny Despina Filippidis Douglas Creighton Saeid Nahavandi Vinod Puri Centre for Intelligent Systems Research (CISR) Deakin University Australia Defence Science and Technology Organisation (DSTO) Department of Defence Australia

this paper presents the comparison between the Microsoft Kinect depth sensor and the Asus Xtion for computer vision applications. Depth sensors, known as RGBD cameras, project an infrared pattern and calculate the depth from the reflected light using an infrared sensitive camera. In this research, we compare the depth sensing capabilities of the two sensors under various conditions. the purpose is to give the reader a background to whether use the Microsoft Kinect or Asus Xtion sensor to solve a specific computer vision problem. the properties of the two depth sensors were investigated by conducting a series of experiments evaluating the accuracy of the sensors under various conditions, which shows the advantages and disadvantages of both Microsoft Kinect and Asus Xtion sensors.

关键词： Accuracy Entropy Cameras Universal Serial Bus Heating Interference computer vision

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Data decomposition and spatial mixture modeling for part based model

Data decomposition and spatial mixture modeling for part bas...

引用

11th Asian conference on computer vision, ACCV 2012

作者： Zhang, Junge Huang, Yongzhen Huang, Kaiqi Wu, Zifeng Tan, Tieniu National Lab. of Pattern Recognition Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9783642373305

this paper presents a system of data decomposition and spatial mixture modeling for part based models. Recently, many enhanced part based models (with e.g., multiple features, more components or parts) have been proposed. Nevertheless, those enhanced models bring high computation cost together with the risk of over-fitting. To tackle this problem, we propose a data decomposition method for part based models which not only accelerates training and testing process but also improves the performance on average. Besides, the original part based model uses a strict rigid structural model to describe the distribution of each part location. It is not "deformable" enough, especially for those instances with different viewpoints or poses in the same aspect ratio. To address this problem, we present a novel spatial mixture modeling method. the spatial mixture embedded model is then integrated into the proposed data decomposition framework. We evaluate our system on the challenging PASCAL VOC2007 and PASCAL VOC2010 datasets, demonstrating the state-of-the-art performance compared with other related methods in terms of accuracy and efficiency. © 2013 Springer-Verlag.

关键词： Aspect ratio

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Weighted interaction force estimation for abnormality detection in crowd scenes

Weighted interaction force estimation for abnormality detect...

引用

11th Asian conference on computer vision, ACCV 2012

作者： Zhu, Xiaobin Liu, Jing Wang, Jinqiao Fu, Wei Lu, Hanqing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9783642374302

In this paper, we propose a weighted interaction force estimation in the social force model(SFM)-based framework, in which the properties of surrounding individuals in terms of motion consistence, distance apart, and angle-of-view along moving directions are fully utilized in order to more precisely discriminate normal or abnormal behaviors of crowd. To avoid the challenges in object tracking in crowded videos, we first perform particle advection to capture the continuity of crowd flow and use these moving particles as individuals for the interaction force estimation. For a more reasonable interaction force estimation, we jointly consider the properties of surrounding individuals, assuming that the individuals with consistent motion (as a particle group) and the ones out of the angle-of-view have no influence on each other, besides the farther apart ones have weaker influence. In particular, particle groups are clustered by spectral clustering algorithm, in which a novel and high discriminative gait feature in frequency domain, combined with spatial and motion feature, is used. the estimated interaction forces are mapped to image span to form force flow, from which bag-of-word features are extracted. Sparse Topical Coding (STC) model is used to find abnormal events. Experiments conducted on three datasets demonstrate the promising performance of our work against other related ones. © 2013 Springer-Verlag.

关键词： Frequency domain analysis

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