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Joint dictionary learning and topic modeling for image clustering

Joint dictionary learning and topic modeling for image clust...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Lingbo Li Mingyuan Zhou Eric Wang Lawrence Carin Department of Electrical and Computer Engineering Duke University Durham NC USA

A new Bayesian model is proposed, integrating dictionary learning and topic modeling into a unified framework. The model is applied to cluster multiple images, and a subset of the images may be annotated. Example results are presented on the MNIST digit data and on the Microsoft MSRC multi-scene image data. These results reveal the working mechanisms of the model and demonstrate state-of-the-art performance.

关键词： Dictionaries Feature extraction Bayesian methods Computational modeling computer vision Image coding pattern recognition

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Manifold based kernel optimization for KPCA

Manifold based kernel optimization for KPCA

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International Conference on Communication Software and Networks, ICCSN

作者： Li Zeng Bin Chen Linping Du Kejia Xu Department of Machine Vision Institute of Chengdu Computer Application Chinese Academy and Sciences Chengdu China

This paper presents a manifold based kernel optimizing algorithm for KPCA which has recently shown effectiveness for pattern recognition and systematic classification based on extracting nonlinear features. However, their performances largely depend on the kernel function. Current methods simply choose the kernel function empirically or experimentally from a given set of candidates. We use manifold learning to improve the kernel function, which is capable to discover the nonlinear degrees of freedom that underlie complex natural observations. In contrast to previous algorithms for kernel optimization, ours efficiently computes a globally optimal solution that is guaranteed to converge asymptotically to the true structure and extracts the nonlinear features better. Experiments show that the method performed well in the field of pattern recognition.

关键词： Manifolds Proposals Classification algorithms

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Saliency estimation using a non-parametric low-level vision model

Saliency estimation using a non-parametric low-level vision ...

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Conference on computer vision and pattern recognition (CVPR)

作者： Naila Murray Maria Vanrell Xavier Otazu C. Alejandro Parraga Computer Vision Center Computer Science Department Universitat Autònoma Barcelona Barcelona Spain

Many successful models for predicting attention in a scene involve three main steps: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. However, integrating spatial information and justifying the choice of various parameter values remain open problems. In this paper we show that an efficient model of color appearance in human vision, which contains a principled selection of parameters as well as an innate spatial pooling mechanism, can be generalized to obtain a saliency model that outperforms state-of-the-art models. Scale integration is achieved by an inverse wavelet transform over the set of scale-weighted center-surround responses. The scale-weighting function (termed ECSF) has been optimized to better replicate psychophysical data on color appearance, and the appropriate sizes of the center-surround inhibition windows have been determined by training a Gaussian Mixture Model on eye-fixation data, thus avoiding ad-hoc parameter selection. Additionally, we conclude that the extension of a color appearance model to saliency estimation adds to the evidence for a common low-level visual front-end for different visual tasks.

关键词： Image color analysis Computational modeling Wavelet transforms Predictive models Visualization Brightness

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Robust object tracking via online learning of adaptive appearance manifold

Robust object tracking via online learning of adaptive appea...

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International Conference on computer vision Workshops (ICCV Workshops)

作者： Jianwei Ding Yongzhen Huang Kaiqi Huang Tieniu Tan National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781467300629

Appearance modeling plays a critical role in robust object tracking, which should be adaptive to various appearance changes. We propose a new appearance model based on adaptive appearance manifold for object tracking. The adaptive appearance manifold consists of several submanifolds and each is approximated with a low dimensional linear subspace. The initial appearance model is constructed using location information of target object in the first frame, and no prior knowledge is needed. We design an efficient dynamic structure for the adaptive appearance manifold, which can reduce time of comparison between a new observation and the appearance model. The appearance model is incrementally learned online using the input sequence image. We integrate our new appearance model with the particle filtering framework. Several public challenging videos are used to test our tracking algorithm. The experimental results demonstrate that our algorithm is robust to illumination change, pose variation, partial occlusion and clutter background. And the speed of our algorithm is also very fast.

关键词： Target tracking Manifolds Adaptation models Lighting Robustness Videos

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Asymmetric distances for binary embeddings

Asymmetric distances for binary embeddings

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Conference on computer vision and pattern recognition (CVPR)

作者： Albert Gordo Florent Perronnin Computer Vision Center Universitat Autònoma Barcelona Spain Textual and Visual Pattern Analysis Xerox Research Centre Europe France

In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH) and Semi-Supervised Hashing (SSH). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques. We also propose a novel simple binary embedding technique - PCA Embedding (PCAE) - which is shown to yield competitive results with respect to more complex algorithms such as SH and SSH.

关键词： Databases Kernel Euclidean distance Principal component analysis Approximation methods Table lookup Random access memory

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Advanced Intelligent Computing Theories and Applications 2012

引用

丛书名： Lecture Notes in computer Science

2012年

作者： De-Shuang Huang Yong Gan Phalguni Gupta M. Michael Gromiha

ISBN: (数字)9783642259449

ISBN: (纸本)9783642259432

This book constitutes the thoroughly refereed post-conference proceedings of the 7th International Conference on Intelligent Computing, ICIC 2011, held in Zhengzhou, China, in August 2011. The 94 revised full papers presented were carefully reviewed and selected from 832 submissions. The papers are organized in topical sections on intelligent computing in scheduling; local feature descriptors for image processing and recognition; combinatorial and numerical optimization; machine learning theory and methods; intelligent control and automation; knowledge representation/reasoning and expert systems; intelligent computing in pattern recognition; intelligent computing in image processing; intelligent computing in computer vision; biometrics with applications to individual security/forensic sciences; modeling, theory, and applications of positive systems; sparse manifold learning methods and applications; advances in intelligent information processing.

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Color constancy and non-uniform illumination: Can existing algorithms work?

Color constancy and non-uniform illumination: Can existing a...

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International Conference on computer vision Workshops (ICCV Workshops)

作者： Michael Bleier Christian Riess Shida Beigpour Eva Eibenberger Elli Angelopoulou Tobias Tröger André Kaup Pattern Recognition Lab University of Erlangen-Nuremberg Germany Computer Vision Center Universidad Autónoma de Barcelona Spain Multimedia Communications and Signal Processing University of Erlangen-Nuremberg Germany

The color and distribution of illuminants can significantly alter the appearance of a scene. The goal of color constancy (CC) is to remove the color bias introduced by the illuminants. Most existing CC algorithms assume a uniformly illuminated scene. However, more often than not, this assumption is an insufficient approximation of real-world illumination conditions (multiple light sources, shadows, interreflections, etc.). Thus, illumination should be locally determined, taking under consideration that multiple illuminants may be present. In this paper we investigate the suitability of adapting 5 state-of-the-art color constancy methods so that they can be used for local illuminant estimation. Given an arbitrary image, we segment it into superpixels of approximately similar color. Each of the methods is applied independently on every superpixel. For improved accuracy, these independent estimates are combined into a single illuminant-color value per superpixel. We evaluated different fusion methodologies. Our experiments indicate that the best performance is obtained by fusion strategies that combine the outputs of the estimators using regression.

关键词： Image color analysis Lighting Databases Light sources Image segmentation Estimation Bayesian methods

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Robust airlight estimation for haze removal from a single image

Robust airlight estimation for haze removal from a single im...

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IEEE computer Society Conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Matteo Pedone Janne Heikkilä Machine Vision Group University of Oulu Finland

Present methods for haze removal from a single image require the estimation of two physical quantities which, according to the commonly used atmospheric scattering model, are transmission and airlight. The visual quality of images de-hazed with such methods is highly dependent on the accuracy of estimation of the aforementioned quantities. In this paper we propose a new method for reliable airlight color estimation that could be used in digital cameras to automatically de-haze images by removing unrealistic color artifacts. The main idea of our method is based on novel statistics gathered from natural images regarding frequently occurring airlight colors. The statistics are used to introduce a minimization cost functional which has a closed form solution, and is easy to compute. We compare our approach with current methods present in literature, and show its superior robustness with both images with artificially added haze, and real hazy photos.

关键词： Image color analysis Estimation Robustness Atmospheric modeling Scattering Minimization Channel estimation

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Low resolution facial image recognition via multiple kernel criterion

Low resolution facial image recognition via multiple kernel ...

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Asian Conference on pattern recognition (ACPR)

作者： Chuan-Xian Ren Dao-Qing Dai Hong Yan Center of Computer Vision and Department of Mathematics Sun Yat-Sen University Guangzhou China Department of Electric Engineering City University of Hong Kong Hong Kong China

Practical face recognition systems are sometimes confronted with low-resolution (LR) images. Most existing feature extraction algorithms aim to preserve relational structure among objects of the input space in a linear embedding space. However, it has been a consensus that such complex visual learning tasks will be well be solved by adopting multiple descriptors to more precisely characterize the data for improving performance. In this paper, we addresses the problem of matching LR and high-resolution images that are difficult for conventional methods in practice due to the lack of an efficient similarity measure, and a multiple kernel criterion (MKC) is proposed for LR face recognition without any super-resolution (SR) preprocessing. Different image descriptors including RsL2, LBP, Gradientface and IMED are considered as the multiple kernel generators and the Gaussian function is exploited as the distance induced kernel. MKC solves this problem by minimizing the inconsistency between the similarities captured by the multiple kernels, and the nonlinear objective function can be alternatively minimized by a constrained eigenvalue decomposition. Experiments on benchmark databases show that our MKC method indeed improves the recognition performance.

关键词： Kernel Image resolution Face recognition Face Feature extraction Databases Optimization

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Real-time RGB-D mapping and 3-D modeling on the GPU using the random ball cover data structure

Real-time RGB-D mapping and 3-D modeling on the GPU using th...

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International Conference on computer vision Workshops (ICCV Workshops)

作者： Dominik Neumann Felix Lugauer Sebastian Bauer Jakob Wasza Joachim Hornegger Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

The modeling of three-dimensional scene geometry from temporal point cloud streams is of particular interest for a variety of computer vision applications. With the advent of RGB-D imaging devices that deliver dense, metric and textured 6-D data in real-time, on-the-fly reconstruction of static environments has come into reach. In this paper, we propose a system for real-time point cloud mapping based on an efficient implementation of the iterative closest point (ICP) algorithm on the graphics processing unit (GPU). In order to achieve robust mappings at real-time performance, our nearest neighbor search evaluates both geometric and photometric information in a direct manner. For acceleration of the search space traversal, we exploit the inherent computing parallelism of GPUs. In this work, we have investigated the fitness of the random ball cover (RBC) data structure and search algorithm, originally proposed for high-dimensional problems, for 6-D data. In particular, we introduce a scheme that enables both fast RBC construction and queries. The proposed system is validated on an indoor scene modeling scenario. For dense data from the Microsoft Kinect sensor (640 × 480 px), our implementation achieved ICP runtimes of

关键词： Iterative closest point algorithm Graphics processing unit Runtime Nearest neighbor searches Real time systems Data structures Solid modeling

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