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159
Translation insensitive image similarity in complex wavelet domain
 In Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP ’05). IEEE International Conference on
, 2005
"... We propose a complex wavelet domain image similarity measure, which is simultaneously insensitive to luminance change, contrast change and spatial translation. The key idea is to make use of the fact that these image distortions lead to consistent magnitude and/or phase changes of local wavelet coef ..."
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Cited by 70 (8 self)
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We propose a complex wavelet domain image similarity measure, which is simultaneously insensitive to luminance change, contrast change and spatial translation. The key idea is to make use of the fact that these image distortions lead to consistent magnitude and/or phase changes of local wavelet coefficients. Since small scaling and rotation of images can be locally approximated by translation, the proposed measure also shows robustness to spatial scaling and rotation when these geometric distortions are small relative to the size of the wavelet filters. Compared with previous methods, the proposed measure is computationally efficient, and can evaluate the similarity of two images without a precise registration process at the front end. 1.
Joint manifold distance: A new approach to appearance based clustering
 IEEE Computer Society Conference on Computer Vision and Patten Recognition
, 2003
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KLocal Hyperplane and Convex Distance Nearest Neighbor Algorithms
 Advances in Neural Information Processing Systems
, 2001
"... Guided by an initial idea of building a complex (non linear) decision surface with maximal local margin in input space, we give a possible geometrical intuition as to why KNearest Neighbor (KNN) algorithms often perform more poorly than SVMs on classification tasks. We then propose modified KN ..."
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Cited by 48 (4 self)
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Guided by an initial idea of building a complex (non linear) decision surface with maximal local margin in input space, we give a possible geometrical intuition as to why KNearest Neighbor (KNN) algorithms often perform more poorly than SVMs on classification tasks. We then propose modified KNearest Neighbor algorithms to overcome the perceived problem. The approach is similar in spirit to Tangent Distance, but with invariances inferred from the local neighborhood rather than prior knowledge. Experimental results on real world classification tasks suggest that the modified KNN algorithms often give a dramatic improvement over standard KNN and perform as well or better than SVMs.
Experiments with an extended tangent distance
 15th International Conference on Pattern Recognition
"... Invariance is an important aspect in image object recognition. We present results obtained with an extended tangent distance incorporated in a kernel density based Bayesian classifier to compensate for affine image variations. An image distortion model for local variations is introduced and its rela ..."
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Cited by 42 (21 self)
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Invariance is an important aspect in image object recognition. We present results obtained with an extended tangent distance incorporated in a kernel density based Bayesian classifier to compensate for affine image variations. An image distortion model for local variations is introduced and its relationship to tangent distance is considered. The proposed classification algorithms are evaluated on databases of different domains. An excellent result of 2.2 % error rate on the original USPS handwritten digits recognition task is obtained. On a database of radiographs from daily routine, best results are obtained by combining tangent distance and the proposed distortion model. 1.
Manifold Parzen Windows
 Advances in Neural Information Processing Systems 15
, 2002
"... The similarity between objects is a fundamental element of many learning algorithms. Most nonparametric methods take this similarity to be fixed, but much recent work has shown the advantages of learning it, in particular to exploit the local invariances in the data or to capture the possibly n ..."
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Cited by 40 (10 self)
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The similarity between objects is a fundamental element of many learning algorithms. Most nonparametric methods take this similarity to be fixed, but much recent work has shown the advantages of learning it, in particular to exploit the local invariances in the data or to capture the possibly nonlinear manifold on which most of the data lies. We propose a new nonparametric kernel density estimation method which captures the local structure of an underlying manifold through the leading eigenvectors of regularized local covariance matrices. Experiments in density estimation show significant improvements with respect to Parzen density estimators. The density estimators can also be used within Bayes classifiers, yielding classification rates similar to SVMs and much superior to the Parzen classifier.
Tangent Distance Kernels for Support Vector Machines
 IN PROCEEDINGS OF THE 16TH ICPR
, 2002
"... When dealing with pattern recognition problems one encounters different types of apriori knowledge. It is important to incorporate such knowledge into the classification method at hand. A very common type of apriori knowledge is transformation invariance of the input data, e.g. geometric transform ..."
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Cited by 40 (9 self)
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When dealing with pattern recognition problems one encounters different types of apriori knowledge. It is important to incorporate such knowledge into the classification method at hand. A very common type of apriori knowledge is transformation invariance of the input data, e.g. geometric transformations of imagedata like shifts, scaling etc. Distance based classification methods can make use of this by a modified distance measure called tangent distance [13, 14]. We introduce a new class of kernels for support vector machines which incorporate tangent distance and therefore are applicable in cases where such transformation invariances are known. We report experimental results which show that the performance of our method is comparable to other stateoftheart methods, while problems of existing ones are avoided.
Face detection based on multiblock lbp representation. ICB
, 2007
"... Abstract. Effective and realtime face detection has been made possible by using the method of rectangle Haarlike features with AdaBoost learning since Viola and Jones ’ work [12]. In this paper, we present the use of a new set of distinctive rectangle features, called Multiblock Local Binary Pat ..."
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Cited by 36 (5 self)
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Abstract. Effective and realtime face detection has been made possible by using the method of rectangle Haarlike features with AdaBoost learning since Viola and Jones ’ work [12]. In this paper, we present the use of a new set of distinctive rectangle features, called Multiblock Local Binary Patterns (MBLBP), for face detection. The MBLBP encodes rectangular regions ’ intensities by local binary pattern operator, and the resulting binary patterns can describe diverse local structures of images. Based on the MBLBP features, a boostingbased learning method is developed to achieve the goal of face detection. To deal with the nonmetric feature value of MBLBP features, the boosting algorithm uses multibranch regression tree as its weak classifiers. The experiments show the weak classifiers based on MBLBP are more discriminative than Haarlike features and original LBP features. Given the same number of features, the proposed face detector illustrates 15 % higher correct rate at a given false alarm rate of 0.001 than haarlike feature and 8 % higher than original LBP feature. This indicates that MBLBP features can capture more information about the image structure and show more distinctive performance than traditional haarlike features, which simply measure the differences between rectangles. Another advantage of MBLBP feature is its smaller feature set, this makes much less training time. 1
Dropout Training as Adaptive Regularization
"... Dropout and other feature noising schemes control overfitting by artificially corrupting the training data. For generalized linear models, dropout performs a form of adaptive regularization. Using this viewpoint, we show that the dropout regularizer is firstorder equivalent to an L2 regularizer a ..."
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Cited by 31 (3 self)
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Dropout and other feature noising schemes control overfitting by artificially corrupting the training data. For generalized linear models, dropout performs a form of adaptive regularization. Using this viewpoint, we show that the dropout regularizer is firstorder equivalent to an L2 regularizer applied after scaling the features by an estimate of the inverse diagonal Fisher information matrix. We also establish a connection to AdaGrad, an online learning algorithm, and find that a close relative of AdaGrad operates by repeatedly solving linear dropoutregularized problems. By casting dropout as regularization, we develop a natural semisupervised algorithm that uses unlabeled data to create a better adaptive regularizer. We apply this idea to document classification tasks, and show that it consistently boosts the performance of dropout training, improving on stateoftheart results on the IMDB reviews dataset. 1
Incorporating prior knowledge in support vector machines for classification: A review
 Grenoble University
, 1992
"... For classification, support vector machines (SVMs) have recently been introduced and quickly became the state of the art. Now, the incorporation of prior knowledge into SVMs is the key element that allows to increase the performance in many applications. This paper gives a review of the current stat ..."
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Cited by 29 (4 self)
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For classification, support vector machines (SVMs) have recently been introduced and quickly became the state of the art. Now, the incorporation of prior knowledge into SVMs is the key element that allows to increase the performance in many applications. This paper gives a review of the current state of research regarding the incorporation of two general types of prior knowledge into SVMs for classification. The particular forms of prior knowledge considered here are presented in two main groups: classinvariance and knowledge on the data. The first one includes invariances to transformations, to permutations and in domains of input space, whereas the second one contains knowledge on unlabeled data, the imbalance of the training set or the quality of the data. The methods are then described and classified in the three categories that have been used in literature: sample methods based on the modification of the training data, kernel methods based on the modification of the kernel and optimization methods based on the modification of the problem formulation. A recent method, developed for support vector regression, considers prior knowledge on arbitrary regions of the input space. It is exposed here when applied to the classification case. A discussion is then conducted to regroup sample and optimization methods under a regularization framework.
Support Vector Machine Classification with Indefinite Kernels
, 2009
"... We propose a method for support vector machine classification using indefinite kernels. Instead of directly minimizing or stabilizing a nonconvex loss function, our algorithm simultaneously computes support vectors and a proxy kernel matrix used in forming the loss. This can be interpreted as a pena ..."
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Cited by 28 (1 self)
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We propose a method for support vector machine classification using indefinite kernels. Instead of directly minimizing or stabilizing a nonconvex loss function, our algorithm simultaneously computes support vectors and a proxy kernel matrix used in forming the loss. This can be interpreted as a penalized kernel learning problem where indefinite kernel matrices are treated as noisy observations of a true Mercer kernel. Our formulation keeps the problem convex and relatively large problems can be solved efficiently using the projected gradient or analytic center cutting plane methods. We compare the performance of our technique with other methods on several standard data sets.