This page is outdated. Please visit my new page.

Kate Saenko, Brian Kulis, Mario Fritz, Trevor Darrell: Adapting Visual Category Models to New Domains (ECCV'10) Domain adaptation is an important emerging topic in com- puter vision. In this paper, we present one of the first studies of domain shift in the context of object recognition. We introduce a method that adapts object models acquired in a particular visual domain to new imag- ing conditions by learning a transformation that minimizes the effect of domain-induced changes in the feature distribution. The transformation is learned in a supervised manner and can be applied to categories for which there are no labeled examples in the new domain. While we focus our evaluation on object recognition tasks, the transform-based adaptation technique we develop is general and could be applied to non-image data. Another contribution is a new multi-domain object database, freely available for download. We experimentally demonstrate the ability of our method to improve recognition on categories with few or no target domain labels and moderate to large changes in the imaging conditions.

Mario Fritz, Michael Black, Gary Bradski and Trevor Darrell: An Additive Latent Feature Model for Transparent Object Recognition (NIPS'09) Existing methods for visual recognition based on quantized local features can perform poorly when local features exist on transparent surfaces, such as glass or plastic objects. There are characteristic patterns to the local appearance of transparent objects, but they may not be well captured by distances to individual examples or by a local pattern codebook obtained by vector quantization. The appearance of a transparent patch is determined in part by the refraction of a background pattern through a transparent medium: the energy from the background usually dominates the patch appearance. We model transparent local patch appearance using an additive model of latent factors: background factors due to scene content, and factors which capture a local edge energy distribution characteristic of the refraction. We implement our method using a novel LDA-SIFT formulation which performs LDA prior to any vector quantization step; we discover latent topics which are characteristic of particular transparent patches and quantize the SIFT space into transparent visual words according to the latent topic dimensions. No knowledge of the background scene is required at test time; we show examples recognizing transparent glasses in a domestic environment.

Paul Schnitzspan, Mario Fritz, Stefan Roth and Bernt Schiele: Discriminative Structure Learning of Hierarchical Representations for Object Detection (CVPR'09) A variety of flexible models have been proposed to detect objects in challenging real world scenes. Motivated by some of the most successful techniques, we propose a hierarchical multi-feature representation and automatically learn flexible hierarchical object models for a wide variety of object classes. To that end we not only rely on automatic selection of relevant individual features, but go beyond previous work by automatically selecting and modeling complex, long-range feature couplings within this model. To achieve this generality and flexibility our work combines structure learning in conditional random fields and discriminative parameter learning of classifiers using hierarchical features. We adopt an efficient gradient based heuristic for model selection and carry it forward to discriminative, multidimensional selection of features and their couplings for improved detection performance. Experimentally we consistently outperform the currently leading method on all 20 classes of the PASCAL VOC 2007 challenge and achieve the best published results on 16 of 20 classes.

Mario Fritz, Bernt Schiele: Decomposition, Discovery and Detection of Visual Categories Using Topic Models (CVPR'08) We present a novel method for the discovery and detection of visual object categories based on decompositions using topic models. The approach is capable of learning a compact and low dimensional representation for multiple visual categories from multiple view points without labeling of the training instances. The learnt object components range from local structures over line segments to global silhouette-like descriptions. This representation can be used to discover object categories in a totally unsupervised fashion. Furthermore we employ the representation as the basis for building a supervised multi-category detection system making efficient use of training examples and outperforming pure features-based representations. The proposed speed-ups make the system scale to large databases. Experiments on three databases show that the approach improves the state-of-the-art in unsupervised learning as well as supervised detection. In particular we improve the stateof- the-art on the challenging PASCAL’06 multi-class detection tasks for several categories.

Tâm Huynh, Mario Fritz and Bernt Schiele. Discovery of Activity Patterns using Topic Models (UbiComp'08) In this work we propose a novel method to recognize daily routines as a probabilistic combination of activity patterns. The use of topic models enables the automatic discovery of such patterns in a user’s daily routine. We report experimental results that show the ability of the approach to model and recognize daily routines without user annotation.

Paul Schnitzspan, Mario Fritz and Bernt Schiele: Hierarchical Support Vector Random Fields: Joint Training to Combine Local and Global Features (ECCV'08) Recently, impressive results have been reported for the detection of objects in challenging real-world scenes. Interestingly however, the underlying models vary greatly even between the most successful approaches. Methods using a global feature descriptor paired with discriminative classifiers such as SVMs enable high levels of performance, but require large amounts of training data and typically degrade in the presence of partial occlusions. Local feature-based approaches are more robust in the presence of partial occlusions but often produce a significant number of false positives. This paper proposes a novel approach called hierarchical support vector random field that allows 1) to combine the power of global feature-based approaches with the flexibility of local feature-based methods in one consistent multi-layer framework and 2) to automatically learn the tradeoff and the optimal interplay between local, semi-local and global feature contributions. Experiments show that both the combination of local and global features as well as the joint training result in improved detection performance on challenging datasets.

Edgar Seemann, Mario Fritz, Bernt Schiele: Towards Robust Pedestrian Detection in Crowded Image Sequences (CVPR'07)

Object class detection in scenes of realistic complexity remains a challenging task in computer vision. Most recent approaches focus on a single and general model for object class detection. However, in particular in the context of image sequences, it may be advantageous to adapt the general model to a more object–instance speci c model in order to detect this particular object reliably within the image sequence. In this work we present a generative object model that is capable to scale from a general object class model to a more speci c object–instance model. This allows to detect class instances as well as to distinguish between individual object instances reliably. We experimentally evaluate the performance of the proposed system on both still images and image sequences.

Mario Fritz, Bastian Leibe, Barbara Caputo, Bernt Schiele: Integrating Representative and Discriminant Models for Object Category Detection (ICCV'05) Category detection is a lively area of research. While categorization algorithms tend to agree in using local descriptors, they differ in the choice of the classifier, with some using generative models and others discriminative approaches. This paper presents a method for object category detection which integrates a generative model with a discriminative classifier. For each object category, we generate an appearance codebook, which becomes a common vocabulary for the generative and discriminative methods. Given a query image, the generative part of the algorithm finds a set of hypotheses and estimates their support in location and scale. Then, the discriminative part verifies each hypothesis on the same codebook activations. The new algorithm exploits the strengths of both original methods, minimizing their weaknesses. Experiments on several databases show that our new approach performs better than its building blocks taken separately. Moreover, experiments on two challenging multi-scale databases show that our new algorithm outperforms previously reported results.

Eric Hayman, Barbara Caputo, Mario Fritz, Jan-Olof Eklundh: On the Significance of Real-World Conditions for Material Classification (ECCV'04) Classifying materials from their appearance is a challenging problem, especially if illumination and pose conditions are permitted to change: highlights and shadows caused by 3D structure can radically alter a sample’s visual texture. Despite these difficulties, researchers have demonstrated impressive results on the CUReT database which contains many images of 61 materials under different conditions. A first contribution of this paper is to further advance the state-of-the- art by applying Support Vector Machines to this problem. To our knowledge, we record the best results to date on the CUReT database. In our work we additionally investigate the effect of scale since robustness to viewing distance and zoom settings is crucial in many real-world situations. In- deed, a material’s appearance can vary considerably as fine-level detail becomes visible or disappears as the camera moves towards or away from the subject. We handle scale-variations using a pure-learning approach, incorporating sam- ples imaged at different distances into the training set. An empirical investigation is conducted to show how the classification accuracy decreases as less scale in- formation is made available during training. Since the CUReT database contains little scale variation, we introduce a new database which images ten CUReT materials at different distances, while also maintaining some change in pose and illumination. The first aim of the database is thus to provide scale variations, but a second and equally important objec- tive is to attempt to recognise different samples of the CUReT materials. For instance, does training on the CUReT database enable recognition of another piece of sandpaper? The results clearly demonstrate that it is not possible to do so with any acceptable degree of accuracy. Thus we conclude that impressive results even on a well-designed database such as CUReT, does not imply that material classification is close to being a solved problem under real-world conditions.

©2009 Mario Fritz
Valid XHTML & CSS