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Generalized Multiclass AdaBoost and Its Applications to Multimedia Classification

Generalized Multiclass AdaBoost and Its Applications to Mult...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Wei Hao Jiebo Luo Kodak Research Laboratories Eastman Kodak (Japan) Limited USA

AdaBoost has received considerable attention in the vision and multimedia research community in recent years. It is originally designed for two-class classification problems. To handle multiple classes, many AdaBoost extensions have been developed primarily based on various schemes for reducing multiclass classification to multiple two-class problems. From a statistical prospective, AdaBoost can be viewed as a forward stepwise additive model using an exponential loss function. In this paper, we derive a generalized form of AdaBoost for multiclass classification based on a multiclass exponential loss function. To prove its effectiveness, we benchmarked a number of multimedia problems of different nature. Experimental results show that the new boosting algorithm outperforms other multiclass alternatives. In addition, the generalized boosting algorithm can be used to either boost a multiclass classifier, or build a multiclass classifier from a binary one.

关键词： Boosting Classification algorithms Machine learning algorithms Machine learning Layout Training data computer vision pattern recognition conferences Testing

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Rank-one Projections with Adaptive Margins for Face recognition

Rank-one Projections with Adaptive Margins for Face Recognit...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Dong Xu S. Lin Shuicheng Yan Xiaoou Tang Department of Electrical Engineering Columbia University USA MOE-Microsoft Key Laboratory of Multimedia Computing and Communication & Department of EEIS University of Science and Technology Hefei Anhui China Microsoft Research Asia Beijing China Department of Information Engineering Chinese University of Hong Kong Hong Kong China Beckman Institute University of Illinois Urbana-Champaign Champaign USA

In supervised dimensionality reduction, tensor representations of images have recently been employed to enhance classification of high-dimensional data with small training sets. To handle tensor data, this approach has been formulated with tight restrictions on projection directions that, along with convergence issues and the assumption of Gaussian distributed class data, limits its face recognition performance. To overcome these problems, we propose a method of rank-one projections with adaptive margins (RPAM) that gives a provably convergent solution for tensor data over a more general class of projections, while accounting for margins between samples of different classes. In contrast to previous margin based works which determine margin sample pairs within the original high dimensional space, RPAM instead aims to maximize the margins defined in the expected lower dimensional feature subspace by progressive margin refinement after each rank-one projection. In addition to handling tensor data, vector-based variants of RPAM are presented for linear mappings and for nonlinear mappings using kernel tricks. Comprehensive experimental results demonstrate that RPAM brings significant improvement in face recognition over previous subspace learning techniques.

关键词： Face recognition Tensile stress Linear discriminant analysis Asia computer vision pattern recognition Scattering Laboratories Multimedia computing Data engineering

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ImprovingWeb-based Image Search via Content Based Clustering

ImprovingWeb-based Image Search via Content Based Clustering

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： N. Ben-Haim B. Babenko S. Belongie Department of Computer Science and Engineering University of California San Diego USA

Current image search engines on the web rely purely on the keywords around the images and the filenames, which produces a lot of garbage in the search results. Alternatively, there exist methods for content based image retrieval that require a user to submit a query image, and return images that are similar in content. We propose a novel approach named ReSPEC (Re-ranking Sets of Pictures by Exploiting Consistency), that is a hybrid of the two methods. Our algorithm first retrieves the results of a keyword query from an existing image search engine, clusters the results based on extracted image features, and returns the cluster that is inferred to be the most relevant to the search query. Furthermore, it ranks the remaining results in order of relevance.

关键词： Search engines Image retrieval Clustering algorithms Content based retrieval Feature extraction Object recognition computer science Joining processes Region 2 Humans

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A Simple Bayesian Framework for Content-Based Image Retrieval

A Simple Bayesian Framework for Content-Based Image Retrieva...

引用

conference on computer vision and pattern recognition (CVPR)

作者： K.A. Heller Z. Ghahramani Gatsby Computational Neuroscience Unit University College London UK Machine Learning Department Carnegie Mellon University Pittsburgh PA USA Department of Engineering University of Cambridge UK

We present a Bayesian framework for content-based image retrieval which models the distribution of color and texture features within sets of related images. Given a userspecified text query (e.g. "penguins") the system first extracts a set of images, from a labelled corpus, corresponding to that query. The distribution over features of these images is used to compute a Bayesian score for each image in a large unlabelled corpus. Unlabelled images are then ranked using this score and the top images are returned. Although the Bayesian score is based on computing marginal likelihoods, which integrate over model parameters, in the case of sparse binary data the score reduces to a single matrix-vector multiplication and is therefore extremely efficient to compute. We show that our method works surprisingly well despite its simplicity and the fact that no relevance feedback is used. We compare different choices of features, and evaluate our results using human subjects.

关键词： Bayesian methods Image retrieval Content based retrieval Distributed computing Sparse matrices Image databases Histograms Gabor filters computer vision Educational institutions

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Audio-Visual Foreground Extraction for Event Characterization

Audio-Visual Foreground Extraction for Event Characterizatio...

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： M. Cristani M. Bicego V. Murino Dipartimento di Informatica University of Verona Verona Italy DEIR University of Sassari Sassari Italy

This paper presents a new method able to integrate audio and visual information for scene analysis in a typical surveillance scenario, using only one camera and one monaural microphone. Visual information is analyzed by a standard visual background/foreground (BG/FG) modelling module, enhanced with a novelty detection stage, and coupled with an audio BG/FG modelling scheme. The audiovisual association is performed on-line, by exploiting the concept of synchrony. Experimental tests carrying out classification and clustering of events show all the potentialities of the proposed approach, also in comparison with the results obtained by using the single modalities.

关键词： Layout Surveillance Information analysis Event detection Data mining Humans pattern recognition Object detection Image analysis Cameras

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Shape Topics: A Compact Representation and New Algorithms for 3D Partial Shape Retrieval

Shape Topics: A Compact Representation and New Algorithms fo...

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conference on computer vision and pattern recognition (CVPR)

作者： Yi Liu Hongbin Zha Hong Qin National Laboratory on Machine Perception Peking University China Department of Computer Science State University of New York Stony Brook USA

This paper develops an efficient new method for 3D partial shape retrieval. First, a Monte Carlo sampling strategy is employed to extract local shape signatures from each 3D model. After vector quantization, these features are represented by using a bag-of-words model. The main contributions of this paper are threefold as follows: 1) a partial shape dissimilarity measure is proposed to rank shapes according to their distances to the input query, without using any timeconsuming alignment procedure; 2) by applying the probabilistic text analysis technique, a highly compact representation "Shape Topics" and accompanying algorithms are developed for efficient 3D partial shape retrieval, the mapping from "Shape Topics" to "object categories" is established using multi-class SVMs; and 3) a method for evaluating the performance of partial shape retrieval is proposed and tested. To our best knowledge, very few existing methods are able to perform well online partial shape retrieval for large 3D shape repositories. Our experimental results are expected to validate the efficacy and effectiveness of our novel approach.

关键词： Shape measurement Text analysis computer vision Vector quantization Biological system modeling computer aided manufacturing Laboratories computer science Monte Carlo methods Testing

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Real Time Localization and 3D Reconstruction

Real Time Localization and 3D Reconstruction

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conference on computer vision and pattern recognition (CVPR)

作者： E. Mouragnon M. Lhuillier M. Dhome F. Dekeyser P. Sayd Image and embedded computer Laboratory CEA/LIST/DTSI/SARC Gif-sur-Yvette France LASMEA UMR 6602 CNRS Université Blaise Pascal Aubiere France

In this paper we describe a method that estimates the motion of a calibrated camera (settled on an experimental vehicle) and the tridimensional geometry of the environment. The only data used is a video input. In fact, interest points are tracked and matched between frames at video rate. Robust estimates of the camera motion are computed in real-time, key-frames are selected and permit the features 3D reconstruction. The algorithm is particularly appropriate to the reconstruction of long images sequences thanks to the introduction of a fast and local bundle adjustment method that ensures both good accuracy and consistency of the estimated camera poses along the sequence. It also largely reduces computational complexity compared to a global bundle adjustment. Experiments on real data were carried out to evaluate speed and robustness of the method for a sequence of about one kilometer long. Results are also compared to the ground truth measured with a differential GPS.

关键词： Cameras Image reconstruction Robustness Robot vision systems Motion estimation Computational geometry Layout Optimization methods Simultaneous localization and mapping Iterative algorithms

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Perceptual Information of Images and the Bias in Homogeneity-based Segmentation

Perceptual Information of Images and the Bias in Homogeneity...

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Hui Zhang S.A. Goldman Department of Computer Science and Engineering Washington University Saint Louis MO USA

Semantic image segmentation aims to partition an image into separate regions, which ideally corresponds to different real-world objects. Many segmentation algorithms have been proposed, exploiting a wide variety of image features and characteristics. It has been shown through empirical studies that segmentation methods that assume a good segmentation partitions an image into different homogeneous regions are likely to fail in non-trivial situations, while methods based on perceptual organization generally generate more favorable segmentations. Yet no formal justification has been provided. In this paper, we propose an information measure for images, the perceptual information, based on human visual perception organization. Using perceptual information, we justify that homogeneity-based segmentation methods are inherently biased, and by incorporating knowledge, perceptual organization can overcome the bias and generate better segmentations.

关键词： Image segmentation Humans Visual perception Partitioning algorithms computer vision computer science Drives Application software Image analysis Object recognition

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Neighborhood Aided Implicit Active Contours

Neighborhood Aided Implicit Active Contours

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conference on computer vision and pattern recognition (CVPR)

作者： Huafeng Liu Yunmei Chen Wufan Chen State Key Laboratory of Modern Optical Instrumentation Zhejiang University Hangzhou China Department of Electrical and Electronic Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong

We have developed a geometric deformable model that employs neighborhood influence to achieve robust segmentation for noisy and broken edges. The fundamental power of this strategy rests with the explicitly combination of regional inter-point constraints, image forces, and a priori boundary information for each geometric contour point within its adaptively determined local influence domain. This formulation thus naturally unifies the essences of the geometric and parametric snakes through automatic local scale selection, and exhibits their respective fundamental strengths of allowing stable boundary detection when the edge information is weak and possibly discontinuous, while maintaining the abilities to handle topological changes during front evolution. In particular, this paper presents an implementation of the method through local integration of the level set function and the image/prior-driven evolution forces, where the resulting partial differential equation is solved numerically using standard finite difference method. Experimental results on synthetic and real images demonstrate its superior performance.

关键词： Active contours Shape Deformable models Laboratories Mathematics Robustness Image segmentation Image edge detection Level set computer vision

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Three-Dimensional Volume Reconstruction Based on Trajectory Fusion from Confocal Laser Scanning Microscope Images

Three-Dimensional Volume Reconstruction Based on Trajectory ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Sang-Chul Lee P. Bajcsy National Center for Supercomputing Applications University of Illinois Urbana-Champaign Urbana-Champaign IL USA

In this paper, we address the problem of 3D volume reconstruction from depth adjacent subvolumes (i.e., sets of image frames) acquired using a confocal laser scanning microscope (CLSM). Our goal is to align sub-volumes by estimating an optimal global image transformation which preserves morphological smoothness of medical structures (called features, e.g., blood vessels) inside of a reconstructed 3D volume. We approached the problem by learning morphological characteristics of structures inside of each sub-volume, i.e. centroid trajectories of features. Next, adjacent sub-volumes are aligned by fusing the morphological characteristics of structures using extrapolation or model fitting. Finally, a global sub-volume to subvolume transformation is computed based on the entire set of fused structures. The trajectory-based 3D volume reconstruction method described here is evaluated with a pair of consecutive physical sections using two evaluation metrics for morphological continu

关键词： Image reconstruction Laser fusion Microscopy Biomedical imaging computer vision Optical distortion Optical imaging Biomedical optical imaging Geometrical optics Blood vessels

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