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The 2005 pascal visual object classes challenge
, 2006
"... Abstract. The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not presegmented objects). Four object classes were selected: motorbikes, bicycles, cars and peop ..."
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Cited by 649 (23 self)
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Abstract. The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not presegmented objects). Four object classes were selected: motorbikes, bicycles, cars and people. Twelve teams entered the challenge. In this chapter we provide details of the datasets, algorithms used by the teams, evaluation criteria, and results achieved. 1
The pyramid match kernel: Discriminative classification with sets of image features
 IN ICCV
, 2005
"... Discriminative learning is challenging when examples are sets of features, and the sets vary in cardinality and lack any sort of meaningful ordering. Kernelbased classification methods can learn complex decision boundaries, but a kernel over unordered set inputs must somehow solve for correspondenc ..."
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Cited by 544 (29 self)
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Discriminative learning is challenging when examples are sets of features, and the sets vary in cardinality and lack any sort of meaningful ordering. Kernelbased classification methods can learn complex decision boundaries, but a kernel over unordered set inputs must somehow solve for correspondences – generally a computationally expensive task that becomes impractical for large set sizes. We present a new fast kernel function which maps unordered feature sets to multiresolution histograms and computes a weighted histogram intersection in this space. This “pyramid match” computation is linear in the number of features, and it implicitly finds correspondences based on the finest resolution histogram cell where a matched pair first appears. Since the kernel does not penalize the presence of extra features, it is robust to clutter. We show the kernel function is positivedefinite, making it valid for use in learning algorithms whose optimal solutions are guaranteed only for Mercer kernels. We demonstrate our algorithm on object recognition tasks and show it to be accurate and dramatically faster than current approaches.
Probability product kernels
 Journal of Machine Learning Research
, 2004
"... The advantages of discriminative learning algorithms and kernel machines are combined with generative modeling using a novel kernel between distributions. In the probability product kernel, data points in the input space are mapped to distributions over the sample space and a general inner product i ..."
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Cited by 180 (9 self)
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The advantages of discriminative learning algorithms and kernel machines are combined with generative modeling using a novel kernel between distributions. In the probability product kernel, data points in the input space are mapped to distributions over the sample space and a general inner product is then evaluated as the integral of the product of pairs of distributions. The kernel is straightforward to evaluate for all exponential family models such as multinomials and Gaussians and yields interesting nonlinear kernels. Furthermore, the kernel is computable in closed form for latent distributions such as mixture models, hidden Markov models and linear dynamical systems. For intractable models, such as switching linear dynamical systems, structured meanfield approximations can be brought to bear on the kernel evaluation. For general distributions, even if an analytic expression for the kernel is not feasible, we show a straightforward sampling method to evaluate it. Thus, the kernel permits discriminative learning methods, including support vector machines, to exploit the properties, metrics and invariances of the generative models we infer from each datum. Experiments are shown using multinomial models for text, hidden Markov models for biological data sets and linear dynamical systems for time series data.
The pyramid match kernel: Efficient learning with sets of features
 Journal of Machine Learning Research
, 2007
"... In numerous domains it is useful to represent a single example by the set of the local features or parts that comprise it. However, this representation poses a challenge to many conventional machine learning techniques, since sets may vary in cardinality and elements lack a meaningful ordering. Kern ..."
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Cited by 136 (10 self)
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In numerous domains it is useful to represent a single example by the set of the local features or parts that comprise it. However, this representation poses a challenge to many conventional machine learning techniques, since sets may vary in cardinality and elements lack a meaningful ordering. Kernel methods can learn complex functions, but a kernel over unordered set inputs must somehow solve for correspondences—generally a computationally expensive task that becomes impractical for large set sizes. We present a new fast kernel function called the pyramid match that measures partial match similarity in time linear in the number of features. The pyramid match maps unordered feature sets to multiresolution histograms and computes a weighted histogram intersection in order to find implicit correspondences based on the finest resolution histogram cell where a matched pair first appears. We show the pyramid match yields a Mercer kernel, and we prove bounds on its error relative to the optimal partial matching cost. We demonstrate our algorithm on both classification and regression tasks, including object recognition, 3D human pose inference, and time of publication estimation for documents, and we show that the proposed method is accurate and significantly more efficient than current approaches.
Kernel Descriptors for Visual Recognition
"... The design of lowlevel image features is critical for computer vision algorithms. Orientation histograms, such as those in SIFT [16] and HOG [3], are the most successful and popular features for visual object and scene recognition. We highlight the kernel view of orientation histograms, and show th ..."
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Cited by 69 (13 self)
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The design of lowlevel image features is critical for computer vision algorithms. Orientation histograms, such as those in SIFT [16] and HOG [3], are the most successful and popular features for visual object and scene recognition. We highlight the kernel view of orientation histograms, and show that they are equivalent to a certain type of match kernels over image patches. This novel view allows us to design a family of kernel descriptors which provide a unified and principled framework to turn pixel attributes (gradient, color, local binary pattern, etc.) into compact patchlevel features. In particular, we introduce three types of match kernels to measure similarities between image patches, and construct compact lowdimensional kernel descriptors from these match kernels using kernel principal component analysis (KPCA) [23]. Kernel descriptors are easy to design and can turn any type of pixel attribute into patchlevel features. They outperform carefully tuned and sophisticated features including SIFT and deep belief networks. We report superior performance on standard image classification benchmarks: Scene15, Caltech101, CIFAR10 and CIFAR10ImageNet. 1
Mercer kernels for object recognition with local features
, 2004
"... A new class of kernels for object recognition based on local image feature representations are introduced in this paper. These kernels satisfy the Mercer condition and incorporate multiple types of local features and semilocal constraints between them. Experimental results of SVM classifiers coupled ..."
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Cited by 64 (0 self)
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A new class of kernels for object recognition based on local image feature representations are introduced in this paper. These kernels satisfy the Mercer condition and incorporate multiple types of local features and semilocal constraints between them. Experimental results of SVM classifiers coupled with the proposed kernels are reported on recognition tasks with the COIL100 database and compared with existing methods. The proposed kernels achieved competitive performance and were robust to changes in object configurations and image degradations. 1.
An SVM learning approach to robotic grasping
 In IEEE International Conference on Robotics and Automation
, 2004
"... Abstract — Finding appropriate stable grasps for a hand (either robotic or human) on an arbitrary object has proved to be a challenging and difficult problem. The space of grasping parameters coupled with the degreesoffreedom and geometry of the object to be grasped creates a highdimensional, non ..."
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Cited by 62 (8 self)
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Abstract — Finding appropriate stable grasps for a hand (either robotic or human) on an arbitrary object has proved to be a challenging and difficult problem. The space of grasping parameters coupled with the degreesoffreedom and geometry of the object to be grasped creates a highdimensional, non smooth manifold. Traditional search methods applied to this manifold are typically not powerful enough to find appropriate stable grasping solutions, let alone optimal grasps. We address this issue in this paper, which attempts to find optimal grasps of objects using a grasping simulator. Our unique approach to the problem involves a combination of numerical methods to recover parts of the grasp quality surface with any robotic hand, and contemporary machine learning methods to interpolate that surface, in order to find the optimal grasp. I.
C.: Efficient match kernels between sets of features for visual recognition
 In: NIPS (2009
"... sminchisescu.ins.unibonn.de In visual recognition, the images are frequently modeled as unordered collections of local features (bags). We show that bagofwords representations commonly used in conjunction with linear classifiers can be viewed as special match kernels, which count 1 if two local f ..."
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Cited by 62 (17 self)
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sminchisescu.ins.unibonn.de In visual recognition, the images are frequently modeled as unordered collections of local features (bags). We show that bagofwords representations commonly used in conjunction with linear classifiers can be viewed as special match kernels, which count 1 if two local features fall into the same regions partitioned by visual words and 0 otherwise. Despite its simplicity, this quantization is too coarse, motivating research into the design of match kernels that more accurately measure the similarity between local features. However, it is impractical to use such kernels for large datasets due to their significant computational cost. To address this problem, we propose efficient match kernels (EMK) that map local features to a low dimensional feature space and average the resulting vectors to form a setlevel feature. The local feature maps are learned so their inner products preserve, to the best possible, the values of the specified kernel function. Classifiers based on EMK are linear both in the number of images and in the number of local features. We demonstrate that EMK are extremely efficient and achieve the current state of the art in three difficult computer vision datasets: Scene15, Caltech101 and Caltech256. 1
Improving “bagofkeypoints” image categorisation
, 2005
"... In this paper we propose two distinct enhancements to the basic “bagofkeypoints ” image categorisation scheme proposed in [4]. In this approach images are represented as a variable sized set of local image features (keypoints). Thus, we require machine learning tools which can operate on sets of v ..."
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Cited by 37 (0 self)
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In this paper we propose two distinct enhancements to the basic “bagofkeypoints ” image categorisation scheme proposed in [4]. In this approach images are represented as a variable sized set of local image features (keypoints). Thus, we require machine learning tools which can operate on sets of vectors. In [4] this is achieved by representing the set as a histogram over bins found by kmeans. We show how this approach can be improved and generalised using Gaussian Mixture Models (GMMs). Alternatively, the set of keypoints can be represented directly as a probability density function, over which a kernel can be defined. This approach is shown to give state of the art categorisation performance.