10 results

2015


Positive definite matrices and the S-divergence

Sra, S.

Proceedings of the American Mathematical Society, 2015, Published electronically: October 22, 2015 (article)

ei

DOI Project Page [BibTex]

2015


DOI Project Page [BibTex]

2011


Efficient Similarity Search for Covariance Matrices via the Jensen-Bregman LogDet Divergence

Cherian, A., Sra, S., Banerjee, A., Papanikolopoulos, N.

In IEEE International Conference on Computer Vision, ICCV 2011, pages: 2399-2406, (Editors: DN Metaxas and L Quan and A Sanfeliu and LJ Van Gool), IEEE, 13th International Conference on Computer Vision (ICCV), 2011 (inproceedings)

ei

DOI Project Page [BibTex]

2011


DOI Project Page [BibTex]


Submodularity beyond submodular energies: coupling edges in graph cuts

Jegelka, S., Bilmes, J.

In pages: 1897-1904, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2011 (inproceedings)

Abstract
We propose a new family of non-submodular global energy functions that still use submodularity internally to couple edges in a graph cut. We show it is possible to develop an efficient approximation algorithm that, thanks to the internal submodularity, can use standard graph cuts as a subroutine. We demonstrate the advantages of edge coupling in a natural setting, namely image segmentation. In particular, for finestructured objects and objects with shading variation, our structured edge coupling leads to significant improvements over standard approaches.

ei

PDF Web DOI Project Page [BibTex]

PDF Web DOI Project Page [BibTex]

2010


Unsupervised Object Discovery: A Comparison

Tuytelaars, T. Lampert, CH. Blaschko, MB. Buntine, W.

International Journal of Computer Vision, 88(2):284-302, June 2010 (article)

Abstract
The goal of this paper is to evaluate and compare models and methods for learning to recognize basic entities in images in an unsupervised setting. In other words, we want to discover the objects present in the images by analyzing unlabeled data and searching for re-occurring patterns. We experiment with various baseline methods, methods based on latent variable models, as well as spectral clustering methods. The results are presented and compared both on subsets of Caltech256 and MSRC2, data sets that are larger and more challenging and that include more object classes than what has previously been reported in the literature. A rigorous framework for evaluating unsupervised object discovery methods is proposed.

ei

PDF DOI Project Page [BibTex]

2010


PDF DOI Project Page [BibTex]


An efficient divide-and-conquer cascade for nonlinear object detection

Lampert, CH.

In Proceedings of the Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010), pages: 1022-1029, IEEE, Piscataway, NJ, USA, Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2010 (inproceedings)

Abstract
We introduce a method to accelerate the evaluation of object detection cascades with the help of a divide-and-conquer procedure in the space of candidate regions. Compared to the exhaustive procedure that thus far is the state-of-the-art for cascade evaluation, the proposed method requires fewer evaluations of the classifier functions, thereby speeding up the search. Furthermore, we show how the recently developed efficient subwindow search (ESS) procedure [11] can be integrated into the last stage of our method. This allows us to use our method to act not only as a faster procedure for cascade evaluation, but also as a tool to perform efficient branch-and-bound object detection with nonlinear quality functions, in particular kernelized support vector machines. Experiments on the PASCAL VOC 2006 dataset show an acceleration of more than 50% by our method compared to standard cascade evaluation.

ei

PDF Web DOI Project Page [BibTex]

PDF Web DOI Project Page [BibTex]

2009


Let the Kernel Figure it Out: Principled Learning of Pre-processing for Kernel Classifiers

Gehler, P., Nowozin, S.

In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages: 2836-2843, IEEE Service Center, Piscataway, NJ, USA, CVPR, June 2009 (inproceedings)

Abstract
Most modern computer vision systems for high-level tasks, such as image classification, object recognition and segmentation, are based on learning algorithms that are able to separate discriminative information from noise. In practice, however, the typical system consists of a long pipeline of pre-processing steps, such as extraction of different kinds of features, various kinds of normalizations, feature selection, and quantization into aggregated representations such as histograms. Along this pipeline, there are many parameters to set and choices to make, and their effect on the overall system performance is a-priori unclear. In this work, we shorten the pipeline in a principled way. We move pre-processing steps into the learning system by means of kernel parameters, letting the learning algorithm decide upon suitable parameter values. Learning to optimize the pre-processing choices becomes learning the kernel parameters. We realize this paradigm by extending the recent Multiple Kernel Learning formulation from the finite case of having a fixed number of kernels which can be combined to the general infinite case where each possible parameter setting induces an associated kernel. We evaluate the new paradigm extensively on image classification and object classification tasks. We show that it is possible to learn optimal discriminative codebooks and optimal spatial pyramid schemes, consistently outperforming all previous state-of-the-art approaches.

ei

PDF Web DOI Project Page [BibTex]

2009


PDF Web DOI Project Page [BibTex]


Learning To Detect Unseen Object Classes by Between-Class Attribute Transfer

Lampert, C., Nickisch, H., Harmeling, S.

In CVPR 2009, pages: 951-958, IEEE Service Center, Piscataway, NJ, USA, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 2009 (inproceedings)

Abstract
We study the problem of object classification when training and test classes are disjoint, i.e. no training examples of the target classes are available. This setup has hardly been studied in computer vision research, but it is the rule rather than the exception, because the world contains tens of thousands of different object classes and for only a very few of them image, collections have been formed and annotated with suitable class labels. In this paper, we tackle the problem by introducing attribute-based classification. It performs object detection based on a human-specified high-level description of the target objects instead of training images. The description consists of arbitrary semantic attributes, like shape, color or even geographic information. Because such properties transcend the specific learning task at hand, they can be pre-learned, e.g. from image datasets unrelated to the current task. Afterwards, new classes can be detected based on their attribute representation, without the need for a new training phase. In order to evaluate our method and to facilitate research in this area, we have assembled a new large-scale dataset, ldquoAnimals with Attributesrdquo, of over 30,000 animal images that match the 50 classes in Osherson's classic table of how strongly humans associate 85 semantic attributes with animal classes. Our experiments show that by using an attribute layer it is indeed possible to build a learning object detection system that does not require any training images of the target classes.

ei

PDF Web DOI Project Page [BibTex]

PDF Web DOI Project Page [BibTex]


Detecting Objects in Large Image Collections and Videos by Efficient Subimage Retrieval

Lampert, CH.

In ICCV 2009, pages: 987-994, IEEE Computer Society, Piscataway, NJ, USA, Twelfth IEEE International Conference on Computer Vision, October 2009 (inproceedings)

Abstract
We study the task of detecting the occurrence of objects in large image collections or in videos, a problem that combines aspects of content based image retrieval and object localization. While most previous approaches are either limited to special kinds of queries, or do not scale to large image sets, we propose a new method, efficient subimage retrieval (ESR), which is at the same time very flexible and very efficient. Relying on a two-layered branch-and-bound setup, ESR performs object-based image retrieval in sets of 100,000 or more images within seconds. An extensive evaluation on several datasets shows that ESR is not only very fast, but it also achieves detection accuracies that are on par with or superior to previously published methods for object-based image retrieval.

ei

PDF Web DOI Project Page [BibTex]

PDF Web DOI Project Page [BibTex]


Global Connectivity Potentials for Random Field Models

Nowozin, S., Lampert, C.

In CVPR 2009, pages: 818-825, IEEE Service Center, Piscataway, NJ, USA, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 2009 (inproceedings)

Abstract
Markov random field (MRF, CRF) models are popular in computer vision. However, in order to be computationally tractable they are limited to incorporate only local interactions and cannot model global properties, such as connectedness, which is a potentially useful high-level prior for object segmentation. In this work, we overcome this limitation by deriving a potential function that enforces the output labeling to be connected and that can naturally be used in the framework of recent MAP-MRF LP relaxations. Using techniques from polyhedral combinatorics, we show that a provably tight approximation to the MAP solution of the resulting MRF can still be found efficiently by solving a sequence of max-flow problems. The efficiency of the inference procedure also allows us to learn the parameters of a MRF with global connectivity potentials by means of a cutting plane algorithm. We experimentally evaluate our algorithm on both synthetic data and on the challenging segmentation task of the PASCAL VOC 2008 data set. We show that in both cases the addition of a connectedness prior significantly reduces the segmentation error.

ei

PDF PDF Web DOI Project Page [BibTex]

PDF PDF Web DOI Project Page [BibTex]

2008


Learning to Localize Objects with Structured Output Regression

Blaschko, MB. Lampert, CH.

In ECCV 2008, pages: 2-15, (Editors: Forsyth, D. A., P. H.S. Torr, A. Zisserman), Springer, Berlin, Germany, 10th European Conference on Computer Vision, October 2008, Best Student Paper Award (inproceedings)

Abstract
Sliding window classifiers are among the most successful and widely applied techniques for object localization. However, training is typically done in a way that is not specific to the localization task. First a binary classifier is trained using a sample of positive and negative examples, and this classifier is subsequently applied to multiple regions within test images. We propose instead to treat object localization in a principled way by posing it as a problem of predicting structured data: we model the problem not as binary classification, but as the prediction of the bounding box of objects located in images. The use of a joint-kernel framework allows us to formulate the training procedure as a generalization of an SVM, which can be solved efficiently. We further improve computational efficiency by using a branch-and-bound strategy for localization during both training and testing. Experimental evaluation on the PASCAL VOC and TU Darmstadt datasets show that the structured training procedure improves pe rformance over binary training as well as the best previously published scores.

ei

PDF Web DOI Project Page [BibTex]

2008


PDF Web DOI Project Page [BibTex]