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Tag-based web photo retrieval improved by batch mode re-tagging

Tag-based web photo retrieval improved by batch mode re-tagg...

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conference on computer vision and pattern recognition (CVPR)

作者： Lin Chen Dong Xu Ivor W. Tsang Jiebo Luo School of Computer Engineering Nanyang Technological University Singapore Kodak Research Laboratories Eastman Kodak (Japan) Limited Rochester USA

Web photos in social media sharing websites such as Flickr are generally accompanied by rich but noisy textual descriptions (tags, captions, categories, etc.). In this paper, we proposed a tag-based photo retrieval framework to improve the retrieval performance for Flickr photos by employing a novel batch mode re-tagging method. The proposed batch mode re-tagging method can automatically refine noisy tags of a group of Flickr photos uploaded by the same user within a short period by leveraging millions of training web images and their associated rich textual descriptions. Specifically, for one group of Flickr photos, we construct a group-specific lexicon which contains only the tags of all photos within the group. For each query tag, we employ the inverted file method to automatically find loosely labeled training web images. We propose a SVM with Augmented Features, referred to as AFSVM, to learn adapted classifiers to refine the annotation tags of photos by leveraging the existing SVM classifiers of popular tags, which are associated with a large amount of positive training web images. Moreover, to further refine the annotation tags of photos in the same group, we additionally introduce an objective function that utilizes the visual similarities of photos within the group as well as the semantic proximities of their tags. Based on the refined tags, photos can be retrieved according to more reliable relevance scores. Extensive experiments demonstrate the effectiveness of our framework.

关键词： Support vector machines Support vector machine classification Testing Degradation Laboratories Discussion forums Digital cameras Information retrieval Content based retrieval Voting

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Recovering fluid-type motions using Navier-Stokes potential flow

Recovering fluid-type motions using Navier-Stokes potential ...

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conference on computer vision and pattern recognition (CVPR)

作者： Feng Li Liwei Xu Philippe Guyenne Jingyi Yu Department of Computer and Information Sciences University of Delaware Newark DE USA Department of Mathematical Sciences University of Delaware Newark DE USA

The classical optical flow assumes that a feature point maintains constant brightness across the frames. For fluid-type motions such as smoke or clouds, the constant brightness assumption does not hold, and accurately estimating the motion flow from their images is difficult. In this paper, we introduce a simple but effective Navier-Stokes (NS) potential flow model for recovering fluid-type motions. Our method treats the image as a wavefront surface and models the 3D potential flow beneath the surface. The gradient of the velocity potential describes the motion flow at every voxel. We first derive a general brightness constraint that explicitly models wavefront (brightness) variations in terms of the velocity potential. We then use a series of partial differential equations to separately model the dynamics of the potential flow. To solve for the potential flow, we use the Dirichlet-Neumann Operator (DNO) to simplify the 3D volumetric velocity potential to 2D surface velocity potential. We approximate the DNO via Taylor expansions and develop a Fourier domain method to efficiently estimate the Taylor coefficients. Finally we show how to use the DNO to recover the velocity potential from images as well as to propagate the wavefront (image) over time. Experimental results on both synthetic and real images show that our technique is robust and reliable.

关键词： Brightness Optical surface waves Surface waves Surface treatment Image motion analysis Clouds Motion estimation Partial differential equations Taylor series Robustness

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Classification and clustering via dictionary learning with structured incoherence and shared features

Classification and clustering via dictionary learning with s...

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conference on computer vision and pattern recognition (CVPR)

作者： Ignacio Ramirez Pablo Sprechmann Guillermo Sapiro Electrical and Computer Engineering University of Minnesota USA

A clustering framework within the sparse modeling and dictionary learning setting is introduced in this work. Instead of searching for the set of centroid that best fit the data, as in k-means type of approaches that model the data as distributions around discrete points, we optimize for a set of dictionaries, one for each cluster, for which the signals are best reconstructed in a sparse coding manner. Thereby, we are modeling the data as a union of learned low dimensional subspaces, and data points associated to subspaces spanned by just a few atoms of the same learned dictionary are clustered together. An incoherence promoting term encourages dictionaries associated to different classes to be as independent as possible, while still allowing for different classes to share features. This term directly acts on the dictionaries, thereby being applicable both in the supervised and unsupervised settings. Using learned dictionaries for classification and clustering makes this method robust and well suited to handle large datasets. The proposed framework uses a novel measurement for the quality of the sparse representation, inspired by the robustness of the ℓ 1 regularization term in sparse coding. In the case of unsupervised classification and/or clustering, a new initialization based on combining sparse coding with spectral clustering is proposed. This initialization clusters the dictionary atoms, and therefore is based on solving a low dimensional eigen-decomposition problem, being applicable to large datasets. We first illustrate the proposed framework with examples on standard image and speech datasets in the supervised classification setting, obtaining results comparable to the state-of-the-art with this simple approach. We then present experiments for fully unsupervised clustering on extended standard datasets and texture images, obtaining excellent performance.

关键词： Dictionaries Atomic measurements Robustness Clustering algorithms Signal processing algorithms Image reconstruction Speech Linear approximation Art Image restoration

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Area-Based Hierarchical Binocular Stereo Matching Algorithm

Area-Based Hierarchical Binocular Stereo Matching Algorithm

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Chinese conference on pattern recognition (CCPR)

作者： Xiaoli Liu Guangzhu Xu Bangjun Lei Shuifa Sun Yuanyuan Fu College of Electrical Engineering and Renewable Energy Three Gorges University Yichang China College of Computer and Information Technology Institute of Intelligent Vision and Image Information Yichang China

A hierarchical stereo matching algorithm with adaptive threshold is presented. The backwards matching strategy with disparity predetermined and the adaptive matching threshold updating method based on candidates matching points are two core components of the algorithm. First, found those initial match points under a predetermined disparity using an initial matching threshold. Second, updated the threshold value by analyzing statistical characteristics of those initial matching points. Then obtained reliable match points using the updated threshold, and removed false matching points by morphological filtering. Third, selected the next predetermined disparity to repeat the above operations. Finally, produced the smooth disparity map using Gaussian filtering operations. Experimental results in the standard test gallery show that the proposed algorithm is more accurate and can generate wider disparity map than traditional methods, and has better utility values.

关键词： Stereo vision Electronic mail computer vision Filtering ieee computer society Manganese Educational institutions

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Visual event recognition in videos by learning from web data

Visual event recognition in videos by learning from web data

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conference on computer vision and pattern recognition (CVPR)

作者： Lixin Duan Dong Xu Ivor W. Tsang Jiebo Luo School of Computer Engineering Nanyang Technological University Singapore Kodak Research Labs Eastman Kodak (Japan) Limited Rochester NY USA

We propose a visual event recognition framework for consumer domain videos by leveraging a large amount of loosely labeled web videos (e.g., from YouTube). First, we propose a new aligned space-time pyramid matching method to measure the distances between two video clips, where each video clip is divided into space-time volumes over multiple levels. We calculate the pair-wise distances between any two volumes and further integrate the information from different volumes with Integer-flow Earth Mover's Distance (EMD) to explicitly align the volumes. Second, we propose a new cross-domain learning method in order to 1) fuse the information from multiple pyramid levels and features (i.e., space-time feature and static SIFT feature) and 2) cope with the considerable variation in feature distributions between videos from two domains (i.e., web domain and consumer domain). For each pyramid level and each type of local features, we train a set of SVM classifiers based on the combined training set from two domains using multiple base kernels of different kernel types and parameters, which are fused with equal weights to obtain an average classifier. Finally, we propose a cross-domain learning method, referred to as Adaptive Multiple Kernel Learning (A-MKL), to learn an adapted classifier based on multiple base kernels and the prelearned average classifiers by minimizing both the structural risk functional and the mismatch between data distributions from two domains. Extensive experiments demonstrate the effectiveness of our proposed framework that requires only a small number of labeled consumer videos by leveraging web data.

关键词： Videos YouTube Kernel Learning systems Support vector machines Support vector machine classification Digital cameras Humans Training data Robustness

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Segmentation approach based on fuzzy Renyi entropy

Segmentation approach based on fuzzy Renyi entropy

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Chinese conference on pattern recognition

作者： Liu, Suo Lan Kong, Li Zhi Wang, Jian Guo School of Information Science and Engineering Changzhou University Changzhou 213164 China Computer Science Dept. Tangshan College Tangshan 063000 China

ISBN: (纸本)9781424472109

A new image segmentation method based on three-dimensional fuzzy partition Renyi entropy was presented. First, on the basis of the theory of fuzzy partition, the fuzzy partition Renyi entropy was defined by using conditional probability and conditional entropy. ACO was used to obtain the optimal parameters to segmentation . The effectiveness of the proposed approach was demonstrated to real vision occasion, and better than other contrast methods. ©2010 ieee.

关键词： Membership functions

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Learning 3D action models from a few 2D videos for view invariant action recognition

Learning 3D action models from a few 2D videos for view inva...

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conference on computer vision and pattern recognition (CVPR)

作者： Pradeep Natarajan Vivek Kumar Singh Ram Nevatia Raytheon BBN Technologies Cambridge MA USA University of Southern California Los Angeles CA USA

ISBN: (数字)9781424469857

ISBN: (纸本)9781424469840

Most existing approaches for learning action models work by extracting suitable low-level features and then training appropriate classifiers. Such approaches require large amounts of training data and do not generalize well to variations in viewpoint, scale and across datasets. Some work has been done recently to learn multi-view action models from Mocap data, but obtaining such data is time consuming and requires costly infrastructure. We present a method that addresses both these issues by learning action models from just a few video training samples. We model each action as a sequence of primitive actions, represented as functions which transform the actor's state. We formulate model learning as a curve-fitting problem, and present a novel algorithm for learning human actions by lifting 2D annotations of a few keyposes to 3D and interpolating between them. Actions are inferred by sampling the models and accumulating the feature weights learned discriminatively using a latent state Perceptron algorithm. We show results comparable to state-of-art on the standard Weizmann dataset, with a much smaller train:test ratio, and also in datasets for visual gesture recognition and cluttered grocery store environments.

关键词： Videos Inference algorithms Sampling methods Tracking Feature extraction Training data Human computer interaction Shape Appropriate technology Data mining

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Mining Research Topics Evolving Over Time Using a Diachronic Multi-source Approach

Mining Research Topics Evolving Over Time Using a Diachronic...

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ieee International conference on Data Mining workshops (ICDM workshops)

作者： Jean-Charles Lamirel Ghada Safi Navesh Priyankar Pascal Cuxac INRIA-TALARIS Project LORIA Nancy France Computer Science Department University of Aleppo Aleppo Syria Computer Science Department Indian Institute of Technology Kanpur India SRDI Research and Development Department INIST-CNRS Nancy France

The acquisition of new scientific knowledge and the evolution of the needs of the society regularly call into question the orientations of research. Means to recall and visualize these evolutions are thus necessary. The existing tools for research survey give only one fixed vision of the research activity, which does not allow performing tasks of dynamic topic mining. The objective of this paper is thus to propose a new incremental approach in order to follow the evolution of research themes and research groups for a scientific discipline given in terms of emergence or decline. These behaviors are detectable by various methods of filtering. However, our choice is made on the exploitation of neural clustering methods in a multi-view context. This new approach makes it possible to take into account the incremental and chronological aspect of information by opening the way to the detection of convergences and divergences of research themes and groups.

关键词： Clustering methods Context Indexes Data models Bayesian methods Cognition Labeling

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Multi-focus image fusion by combining PDTDFB and wavelet transform

Multi-focus image fusion by combining PDTDFB and wavelet tra...

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Chinese conference on pattern recognition

作者： Shang, Zhaowei Pang, Qingkun College of Computer Science University of Chongqing Chongqing 400030 China

ISBN: (纸本)9781424472109

To extract the salient features of the image more effectively and improve the performance of multi-focus image fusion, for the shortcomings of Curvelet which can not capture the texture information, According to the respective advantages of Pyramidal dual-tree directional filter bank and wavelet, this paper proposes multi-focus image fusion algorithm based on PDTDFB and wavelet. Firstly, the multi-focus image is decomposed into different directions of information by PDTDFB, the absolute value of largest selection of fusion rule is used for the low-frequency information. Then high-frequency information is used for further decomposition of discrete wavelet, the absolute value of the largest selection of fusion scheme is also used for the low-frequency information, while the high-frequency of decomposition is obtained by regional energy rule. Experiment and theoretical analysis show that this method is effective in improving the direction information and edge information of the image, meanwhile extract the salient features of the image. The fusion method proposed in this paper has more improvements than the method of curve let combining wavelet transform. vision has made great improvements, and evaluation is better than the latter. ©2010 ieee.

关键词： Discrete wavelet transforms

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Area-based hierarchical binocular stereo matching algorithm

Area-based hierarchical binocular stereo matching algorithm

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Chinese conference on pattern recognition

作者： Liu, Xiaoli Xu, Guangzhu Lei, Bangjun Sun, Shuifa Fu, Yuanyuan College of Computer and Information Technology Institute of Intelligent Vision and Image Information Yichang 443002 China College of Electrical Engineering and Renewable Energy Three Gorges University Yichang 443002 China

ISBN: (纸本)9781424472109

关键词： Bandpass filters

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