Results 1  10
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101
PCASIFT: A more distinctive representation for local image descriptors
, 2004
"... Stable local feature detection and representation is a fundamental component of many image registration and object recognition algorithms. Mikolajczyk and Schmid [14] recently evaluated a variety of approaches and identified the SIFT [11] algorithm as being the most resistant to common image deforma ..."
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Cited by 365 (6 self)
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Stable local feature detection and representation is a fundamental component of many image registration and object recognition algorithms. Mikolajczyk and Schmid [14] recently evaluated a variety of approaches and identified the SIFT [11] algorithm as being the most resistant to common image deformations. This paper examines (and improves upon) the local image descriptor used by SIFT. Like SIFT, our descriptors encode the salient aspects of the image gradient in the feature point's neighborhood; however, instead of using SIFT's smoothed weighted histograms, we apply Principal Components Analysis (PCA) to the normalized gradient patch. Our experiments demonstrate that the PCAbased local descriptors are more distinctive, more robust to image deformations, and more compact than the standard SIFT representation. We also present results showing that using these descriptors in an image retrieval application results in increased accuracy and faster matching.
Survey of clustering data mining techniques
, 2002
"... Accrue Software, Inc. Clustering is a division of data into groups of similar objects. Representing the data by fewer clusters necessarily loses certain fine details, but achieves simplification. It models data by its clusters. Data modeling puts clustering in a historical perspective rooted in math ..."
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Cited by 247 (0 self)
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Accrue Software, Inc. Clustering is a division of data into groups of similar objects. Representing the data by fewer clusters necessarily loses certain fine details, but achieves simplification. It models data by its clusters. Data modeling puts clustering in a historical perspective rooted in mathematics, statistics, and numerical analysis. From a machine learning perspective clusters correspond to hidden patterns, the search for clusters is unsupervised learning, and the resulting system represents a data concept. From a practical perspective clustering plays an outstanding role in data mining applications such as scientific data exploration, information retrieval and text mining, spatial database applications, Web analysis, CRM, marketing, medical diagnostics, computational biology, and many others. Clustering is the subject of active research in several fields such as statistics, pattern recognition, and machine learning. This survey focuses on clustering in data mining. Data mining adds to clustering the complications of very large datasets with very many attributes of different types. This imposes unique
Graph embedding and extension: A general framework for dimensionality reduction
 IEEE Trans. Pattern Anal. Mach. Intell
, 2007
"... Abstract—Over the past few decades, a large family of algorithms—supervised or unsupervised; stemming from statistics or geometry theory—has been designed to provide different solutions to the problem of dimensionality reduction. Despite the different motivations of these algorithms, we present in t ..."
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Cited by 88 (12 self)
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Abstract—Over the past few decades, a large family of algorithms—supervised or unsupervised; stemming from statistics or geometry theory—has been designed to provide different solutions to the problem of dimensionality reduction. Despite the different motivations of these algorithms, we present in this paper a general formulation known as graph embedding to unify them within a common framework. In graph embedding, each algorithm can be considered as the direct graph embedding or its linear/kernel/tensor extension of a specific intrinsic graph that describes certain desired statistical or geometric properties of a data set, with constraints from scale normalization or a penalty graph that characterizes a statistical or geometric property that should be avoided. Furthermore, the graph embedding framework can be used as a general platform for developing new dimensionality reduction algorithms. By utilizing this framework as a tool, we propose a new supervised dimensionality reduction algorithm called Marginal Fisher Analysis in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizes the interclass separability. We show that MFA effectively overcomes the limitations of the traditional Linear Discriminant Analysis algorithm due to data distribution assumptions and available projection directions. Real face recognition experiments show the superiority of our proposed MFA in comparison to LDA, also for corresponding kernel and tensor extensions. Index Terms—Dimensionality reduction, manifold learning, subspace learning, graph embedding framework. 1
Exponential Family PCA for Belief Compression in POMDPs
 In NIPS
, 2003
"... Standard value function approaches to finding policies for Partially Observable Markov Decision Processes (POMDPs) are intractable for large models. This intractability of these algorithms is due to a great extent to their generating an optimal policy over the entire belief space. However, in real P ..."
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Cited by 66 (11 self)
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Standard value function approaches to finding policies for Partially Observable Markov Decision Processes (POMDPs) are intractable for large models. This intractability of these algorithms is due to a great extent to their generating an optimal policy over the entire belief space. However, in real POMDP problems most belief states are highly unlikely, and there is a highly structured, lowdimensional manifold of plausible beliefs embedded in the highdimensional belief space.
Finding Approximate POMDP Solutions Through Belief Compression
, 2003
"... Standard value function approaches to finding policies for Partially Observable Markov Decision Processes (POMDPs) are generally considered to be intractable for large models. The intractability of these algorithms is to a large extent a consequence of computing an exact, optimal policy over the ent ..."
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Cited by 62 (2 self)
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Standard value function approaches to finding policies for Partially Observable Markov Decision Processes (POMDPs) are generally considered to be intractable for large models. The intractability of these algorithms is to a large extent a consequence of computing an exact, optimal policy over the entire belief space. However, in realworld POMDP problems, computing the optimal policy for the full belief space is often unnecessary for good control even for problems with complicated policy classes. The beliefs experienced by the controller often lie near a structured, lowdimensional manifold embedded in the highdimensional belief space. Finding a good approximation to the optimal value function for only this manifold can be much easier than computing the full value function. We introduce a new method for solving largescale POMDPs by reducing the dimensionality of the belief space. We use Exponential family Principal Components Analysis (Collins, Dasgupta, & Schapire, 2002) to represent sparse, highdimensional belief spaces using lowdimensional sets of learned features of the belief state. We then plan only in terms of the lowdimensional belief features. By planning in this lowdimensional space, we can find policies for POMDP models that are orders of magnitude larger than models that can be handled by conventional techniques. We demonstrate the use of this algorithm on a synthetic problem and on mobile robot navigation tasks. 1.
Motionbased Recognition of People in EigenGait Space
, 2002
"... A motionbased, correspondencefree technique for human gait recognition in monocular video is presented. We contend that the planar dynamics of a walking person are encoded in a 2D plot consisting of the pairwise image similarities of the sequence of images of the person, and that gait recognition ..."
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Cited by 44 (1 self)
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A motionbased, correspondencefree technique for human gait recognition in monocular video is presented. We contend that the planar dynamics of a walking person are encoded in a 2D plot consisting of the pairwise image similarities of the sequence of images of the person, and that gait recognition can be achieved via standard pattern classification of these plots. We use background modelling to track the person for a number of frames and extract a sequence of segmented images of the person. The selfsimilarity plot is computed via correlation of each pair of images in this sequence. For recognition, the method applies Principal Component Analysis to reduce the dimensionality of the plots, then uses the knearest neighbor rule in this reduced space to classify an unknown person. This method is robust to tracking and segmentation errors, and to variation in clothing and background. It is also invariant to small changes in camera viewpoint and walking speed. The method is tested on outdoor sequences of 44 people with 4 sequences of each taken on two different days, and achieves a classification rate of 77%. It is also tested on indoor sequences of 7 people walking on a treadmill, taken from 8 different viewpoints and on 7 different days. A classification rate of 78% is obtained for nearfrontoparallel views, and 65% on average over all view.
Sparse Representation For Computer Vision and Pattern Recognition
, 2009
"... Techniques from sparse signal representation are beginning to see significant impact in computer vision, often on nontraditional applications where the goal is not just to obtain a compact highfidelity representation of the observed signal, but also to extract semantic information. The choice of ..."
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Cited by 44 (1 self)
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Techniques from sparse signal representation are beginning to see significant impact in computer vision, often on nontraditional applications where the goal is not just to obtain a compact highfidelity representation of the observed signal, but also to extract semantic information. The choice of dictionary plays a key role in bridging this gap: unconventional dictionaries consisting of, or learned from, the training samples themselves provide the key to obtaining stateoftheart results and to attaching semantic meaning to sparse signal representations. Understanding the good performance of such unconventional dictionaries in turn demands new algorithmic and analytical techniques. This review paper highlights a few representative examples of how the interaction between sparse signal representation and computer vision can enrich both fields, and raises a number of open questions for further study.
See Me, Hear Me: Integrating Automatic Speech Recognition And LipReading
 Proc. Int. Conf. Spoken Lang. Process
, 1994
"... We present recent work on integration of visual information (automatic lipreading) with acoustic speech for better overall speech recognition. A MultiState Time Delay Neural Network performs the recognition of spelled letter sequences taking advantage of lip images from a standard camera. The prob ..."
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Cited by 37 (6 self)
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We present recent work on integration of visual information (automatic lipreading) with acoustic speech for better overall speech recognition. A MultiState Time Delay Neural Network performs the recognition of spelled letter sequences taking advantage of lip images from a standard camera. The problems addressed include efficient but effective representation of the visual information and optimum manner of combining the two modalities when rendering a decision. We show results for several alternatives to direct gray level image as the visual evidence. These are: Principal Components, Linear Discriminants, and DFT coefficients. Dimensionality of the input is decreased by a factor of 12 while maintaining recognition rates. Combination of the visual and acoustic information is performed at three different levels of abstraction. Results suggest that integration of higher order input features works best. On a continuous spelling task, visualalone recognition of 4555%, when combined with a...
Principal Component Analysis based on Robust Estimators of the Covariance or Correlation Matrix: Influence Functions and Efficiencies
 BIOMETRIKA
, 2000
"... A robust principal component analysis can be easily performed by computing the eigenvalues and eigenvectors of a robust estimator of the covariance or correlation matrix. In this paper we derive the influence functions and the corresponding asymptotic variances for these robust estimators of eige ..."
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Cited by 36 (6 self)
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A robust principal component analysis can be easily performed by computing the eigenvalues and eigenvectors of a robust estimator of the covariance or correlation matrix. In this paper we derive the influence functions and the corresponding asymptotic variances for these robust estimators of eigenvalues and eigenvectors. The behavior of several of these estimators is investigated by a simulation study. Finally, the use of empirical influence functions is illustrated by a real data example.
ROBPCA: a New Approach to Robust Principal Component Analysis
, 2003
"... In this paper we introduce a new method for robust principal component analysis. Classical PCA is based on the empirical covariance matrix of the data and hence it is highly sensitive to outlying observations. In the past, two robust approaches have been developed. The first is based on the eigenvec ..."
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Cited by 30 (12 self)
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In this paper we introduce a new method for robust principal component analysis. Classical PCA is based on the empirical covariance matrix of the data and hence it is highly sensitive to outlying observations. In the past, two robust approaches have been developed. The first is based on the eigenvectors of a robust scatter matrix such as the MCD or an Sestimator, and is limited to relatively lowdimensional data. The second approach is based on projection pursuit and can handle highdimensional data. Here, we propose the ROBPCA approach which combines projection pursuit ideas with robust scatter matrix estimation. It yields more accurate estimates at noncontaminated data sets and more robust estimates at contaminated data. ROBPCA can be computed fast, and is able to detect exact fit situations. As a byproduct, ROBPCA produces a diagnostic plot which displays and classifies the outliers. The algorithm is applied to several data sets from chemometrics and engineering.