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检索条件"任意字段=MIPPR 2011: Pattern Recognition and Computer Vision"

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Descriptor Learning Based on Fisher Separation Criterion for Texture Classification 1

引用

10th Asian Conference on computer vision (ACCV)

作者： Guo, Yimo Zhao, Guoying Pietikainen, Matti Xu, Zhengguang Univ Oulu Dept Elect & Informat Engn Machine Vis Grp Oulu Finland Univ Sci & Technol Sch Informat Engn Beijing Peoples R China

ISBN: (数字)9783642193187

ISBN: (纸本)9783642193170

This paper proposes a novel method to deal with the representation issue in texture classification. A learning framework of image descriptor is designed based on the Fisher separation criteria (FSC) to learn most reliable and robust dominant pattern types considering intra-class similarity and inter-class distance. Image structures are thus be described by a new FSC-based learning (FBL) encoding method. Unlike previous handcraft-design encoding methods, such as the LBP and SIFT, supervised learning approach is used to learn an encoder from training samples. We find that such a learning technique can largely improve the discriminative ability and automatically achieve a good tradeoff between discriminative power and efficiency. The commonly used texture descriptor: local binary pattern (LBP) is taken as an example in the paper, so that we then proposed the FBL-LBP descriptor. We benchmark its performance by classifying textures present in the Outex_TC_0012 database for rotation invariant texture classification, KTH-TIPS2 database for material categorization and Columbia-Utrecht (CUReT) database for classification under different views and illuminations. The promising results verify its robustness to image rotation, illumination changes and noise. Furthermore, to validate the generalization to other problems, we extend the application also to face recognition and evaluate the proposed FBI, descriptor on the FERET face database. The inspiring results show that this descriptor is highly discriminative.

关键词： Local binary pattern

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Violence Detection in Video Using computer vision Techniques

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14th International Conference on computer Analysis of Images and patterns (CAIP)

作者： Bermejo Nievas, Enrique Deniz Suarez, Oscar Bueno Garcia, Gloria Sukthankar, Rahul Univ Castilla La Mancha ETSI Ind Avda Camilo Jose Cela S-N E-13071 Ciudad Real Spain Intel Labs Pittsburgh & Robot Inst Pittsburgh PA USA

ISBN: (纸本)9783642236778;9783642236785

Whereas the action recognition community has focused mostly on detecting simple actions like clapping, walking or jogging, the detection of fights or in general aggressive behaviors has been comparatively less studied. Such capability may be extremely useful in some video surveillance scenarios like in prisons, psychiatric or elderly centers or even in camera phones. After an analysis of previous approaches we test the well-known Bag-of-Words framework used for action recognition in the specific problem of fight detection, along with two of the best action descriptors currently available: STIP and MoSIFT. For the purpose of evaluation and to foster research on violence detection in video we introduce a new video database containing 1000 sequences divided in two groups: fights and non-fights. Experiments on this database and another one with fights from action movies show that fights can be detected with near 90% accuracy.

关键词： action recognition fight detection video surveillance

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Warp that smile on your face: Optimal and smooth deformations for face recognition

Warp that smile on your face: Optimal and smooth deformation...

引用

作者： Gass, Tobias Pishchulin, Leonid Dreuw, Philippe Ney, Hermann Computer Vision Laboratory ETH Zurich Switzerland Computer Vision and Multimodal Computing MPI Informatics Saarbruecken Germany Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany

ISBN: (纸本)9781424491407

In this work, we present novel warping algorithms for full 2D pixel-grid deformations for face recognition. Due to high variation in face appearance, face recognition is considered a very difficult task, especially if only a single reference image, for example a mug-shot, per face is available. Usually model-based approaches with additional training data are used to cope with several types of variation occurring in facial imaging. Image warping contrarily yields a distance measure which is invariant with regard to several types of variation. This allows for precise recognition even using only very few reference observations. Due to the computationally complex problem of optimal 2D warping, pseudo-2D warping-based approaches in the past represented strong approximations of the original problem, and were mainly successful on data with low variability or rectified images. We propose a novel 2D warping method which is globally optimal and makes no prior assumtions on the data variability besides two-dimensional smootheness constraints which both avoid local mirroring and gaps and significantly speed up the optimization. Furthermore, we show that occlusion handling is imperative to obtain smooth warpings in a variety of domains. We evaluate our novel algorithm on various well known databases, such as the AR-Face and CMU-PIE database, and provide a detailed comparison to existing warping approaches. We show that by using simple relative 2D constraints, strong local features and a kernel, which is robust w.r.t. occlusions, our computationally complex approaches outperform state-of-the-art results for recognizing faces under varying expressions, occlusions and poses. Most interestingly, we achieve higher accuracy using fewer training instances per class compared to methods learning a model of the 3D shape. © 2011 IEEE.

关键词： Face recognition

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Global temporal registration of multiple non-rigid surface sequences

Global temporal registration of multiple non-rigid surface s...

引用

作者： Huang, Peng Budd, Chris Hilton, Adrian Centre for Vision Speech Signal Processing University of Surrey Guildford GU2 7XH United Kingdom

ISBN: (纸本)9781457703942

In this paper we consider the problem of aligning multiple non-rigid surface mesh sequences into a single temporally consistent representation of the shape and motion. A global alignment graph structure is introduced which uses shape similarity to identify frames for inter-sequence registration. Graph optimisation is performed to minimise the total non-rigid deformation required to register the input sequences into a common structure. The resulting global alignment ensures that all input sequences are resampled with a common mesh structure which preserves the shape and temporal correspondence. Results demonstrate temporally consistent representation of several public databases of mesh sequences for multiple people performing a variety of motions with loose clothing and hair. © 2011 IEEE.

关键词： Mesh generation

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Localizing parts of faces using a consensus of exemplars

Localizing parts of faces using a consensus of exemplars

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作者： Belhumeur, Peter N. Jacobs, David W. Kriegman, David J. Kumar, Neeraj Kriegman-Belhumeur Vision Technologies United States Columbia University United States University of Maryland College Park United States University of California San Diego United States

ISBN: (纸本)9781457703942

We present a novel approach to localizing parts in images of human faces. The approach combines the output of local detectors with a non-parametric set of global models for the part locations based on over one thousand hand-labeled exemplar images. By assuming that the global models generate the part locations as hidden variables, we derive a Bayesian objective function. This function is optimized using a consensus of models for these hidden variables. The resulting localizer handles a much wider range of expression, pose, lighting and occlusion than prior ones. We show excellent performance on a new dataset gathered from the internet and show that our localizer achieves state-of-the-art performance on the less challenging BioID dataset. © 2011 IEEE.

关键词： computers

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A Modified Neutral Point Method for Kernel-Based Fusion of pattern-recognition Modalities with Incomplete Data Sets

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10th International Workshop on Multiple Classifier Systems

作者： Panov, Maxim Tatarchuk, Alexander Mottl, Vadim Windridge, David Moscow Inst Phys & Technol Inst Per 9 Dolgoprudnyi 141700 Moscow Region Russia Comp Ctr Russian Acad Sci Moscow 119991 Russia Univ Surrey Ctr Vision Speech Signal Proc Guildford GU2 7XH Surrey England

ISBN: (纸本)9783642215568

It is commonly the case in multi-modal pattern recognition that certain modality-specific object features are missing in the training set. We address here the missing data problem for kernel-based Support Vector Machines, in which each modality is represented by the respective kernel matrix over the set of training objects, such that the omission of a modality for some object manifests itself as a blank in the modality-specific kernel matrix at the relevant position. We propose to fill the blank positions in the collection of training kernel matrices via a variant of the Neutral Point Substitution (NPS) method, where the term "neutral point" stands for the locus of points defined by the "neutral hyperplane" in the hypothetical linear space produced by the respective kernel. The current method crucially differs from the previously developed neutral point approach in that it is capable of treating missing data in the training set on the same basis as missing data in the test set. It is therefore of potentially much wider applicability. We evaluate the method on the Biosecure DS2 data set.

关键词： pattern recognition

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Contextual saliency

Contextual saliency

引用

2011 IEEE Visual Communications and Image Processing, VCIP 2011

作者： Fan, Jialue Wu, Ying EECS Department Northwestern University Evanston IL 60208 United States

ISBN: (纸本)9781457713200

Matching local salient points is limited in some computer vision problems (e.g., wide baseline point matching and its applications), since local features vary dramatically under large view changes. In this paper, we propose a new definition on salient points which includes context information around the given point. The proposed contextual salient points are extracted based on the geometry structure of the manifold embedded in the image feature space. Experimental results show the benefit of contextual salient points over local salient points. © 2011 IEEE.

关键词： pattern recognition

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Real-time pose invariant logo and pattern detection

Real-time pose invariant logo and pattern detection

引用

Conference on Intelligent Robots and computer vision XXVIII: Algorithms and Techniques

作者： Sidla, Oliver Kottmann, Michal Benesova, Wanda SLR Engn A-8020 Graz Austria Slovak Tech Univ Fac Informat & Informat Technol Bratislava Slovakia

ISBN: (纸本)9780819484154

The detection of pose invariant planar patterns has many practical applications in computer vision and surveillance systems. The recognition of company logos is used in market studies to examine the visibility and frequency of logos in advertisement. Danger signs on vehicles could be detected to trigger warning systems in tunnels, or brand detection on transport vehicles can be used to count company-specific traffic. We present the results of a study on planar pattern detection which is based on keypoint detection and matching of distortion invariant 2d feature descriptors. Specifically we look at the keypoint detectors of type: i) Lowe's DoG approximation from the SURF algorithm, ii) the Harris Corner Detector, iii) the FAST Corner Detector and iv) Lepetit's keypoint detector. Our study then compares the feature descriptors SURF and compact signatures based on Random Ferns: we use 3 sets of sample images to detect and match 3 logos of different structure to find out which combinations of keypoint detector/feature descriptors work well. A real-world test tries to detect vehicles with a distinctive logo in an outdoor environment under realistic lighting and weather conditions: a camera was mounted on a suitable location for observing the entrance to a parking area so that incoming vehicles could be monitored. In this 2 hour long recording we can successfully detect a specific company logo without false positives.

关键词： keypoint detection feature descriptors logo detection

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Action recognition by dense trajectories

Action recognition by dense trajectories

引用

作者： Wang, Heng Kläser, Alexander Schmid, Cordelia Liu, Cheng-Lin National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China LEAR INRIA Grenoble LJK Grenoble France

ISBN: (纸本)9781457703942

Feature trajectories have shown to be efficient for representing videos. Typically, they are extracted using the KLT tracker or matching SIFT descriptors between frames. However, the quality as well as quantity of these trajectories is often not sufficient. Inspired by the recent success of dense sampling in image classification, we propose an approach to describe videos by dense trajectories. We sample dense points from each frame and track them based on displacement information from a dense optical flow field. Given a state-of-the-art optical flow algorithm, our trajectories are robust to fast irregular motions as well as shot boundaries. Additionally, dense trajectories cover the motion information in videos well. We, also, investigate how to design descriptors to encode the trajectory information. We introduce a novel descriptor based on motion boundary histograms, which is robust to camera motion. This descriptor consistently outperforms other state-of-the-art descriptors, in particular in uncontrolled realistic videos. We evaluate our video description in the context of action classification with a bag-of-features approach. Experimental results show a significant improvement over the state of the art on four datasets of varying difficulty, i.e. KTH, YouTube, Hollywood2 and UCF sports. © 2011 IEEE.

关键词： Trajectories

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Improving classifiers with unlabeled weakly-related videos

Improving classifiers with unlabeled weakly-related videos

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作者： Leistner, Christian Godec, Martin Schulter, Samuel Saffari, Amir Werlberger, Manuel Bischof, Horst Computer Vision Laboratory ETH Zurich Switzerland Institute for Computer Graphics and Vision TU Graz Austria Oxford Brookes University and Sony Computer Entertainment London United Kingdom

ISBN: (纸本)9781457703942

Current state-of-the-art object classification systems are trained using large amounts of hand-labeled images. In this paper, we present an approach that shows how to use unlabeled video sequences, comprising weakly-related object categories towards the target class, to learn better classifiers for tracking and detection. The underlying idea is to exploit the space-time consistency of moving objects to learn classifiers that are robust to local transformations. In particular, we use dense optical flow to find moving objects in videos in order to train part-based random forests that are insensitive to natural transformations. Our method, which is called Video Forests, can be used in two settings: first, labeled training data can be regularized to force the trained classifier to generalize better towards small local transformations. Second, as part of a tracking-by-detection approach, it can be used to train a general codebook solely on pair-wise data that can then be applied to tracking of instances of a priori unknown object categories. In the experimental part, we show on benchmark datasets for both tracking and detection that incorporating unlabeled videos into the learning of visual classifiers leads to improved results. © 2011 IEEE.

关键词： Classification (of information)

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