06171 Abstracts Collection -- Content-Based Retrieval

From 23.04.06 to 28.04.06, the Dagstuhl Seminar 06171 `Content-Based Retrieval\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Interactive design of constrained variational curves

A constrained variational curve is a curve that minimizes some energy functional under certain interpolation constraints. Modeling curves using constrained variational principles is attractive, because the designer is not bothered with the precise representation of the curve (e.g. control points). Until now, the modeling of variational curves is mainly done by means of constraints. If such a curve of least energy is deformed locally (e.g. by moving its control points) the concept of energy minimization is lost. In this paper we introduce deform operators with built-in energy terms. We have tested our ideas in a prototype system for modeling uniform B-spline curves

Чудо на Волині

Твір «Кирик» був записаний відомим житомирським збирачем фольклору Василем Кравченком від Мирона Андрусяка із села Стрибіж Пулинської волості Житомирського повіту у 1908 році. Віднайдений твір був опублікований у збірнику «Труды общества исследователей Волыни. Этнографическиє материалы» в Житомирі 1911 року у п’ятому томі (розділ «Проза» , підрозділ VII «Про скарби»). В опублікованому варіанті В. Кравченка спостерігається зміщення частин тексту, що пояснюється ймовірною помилкою при наборі тексту в друкарні. Тому подається реконструйований варіант твору зі збереженням його мовних діалектних особливостей

Ensembles of Novel Visual Keywords Descriptors for Image Categorization

Object recognition systems need effective image descriptors to obtain good performance levels. Currently, the most widely used image descriptor is the SIFT descriptor that computes histograms of orientation gradients around points in an image. A possible problem of this approach is that the number of features becomes very large when a dense grid is used where the histograms are computed and combined for many different points. The current dominating solution to this problem is to use a clustering method to create a visual codebook that is exploited by an appearance based descriptor to create a histogram of visual keywords present in an image. In this paper we introduce several novel bag of visual keywords methods and compare them with the currently dominating hard bag-of-features (HBOF) approach that uses a hard assignment scheme to compute cluster frequencies. Furthermore, we combine all descriptors with a spatial pyramid and two ensemble classifiers. Experimental results on 10 and 101 classes of the Caltech-101 object database show that our novel methods significantly outperform the traditional HBOF approach and that our ensemble methods obtain state-of-the-art performance levels

Learning Class Regularized Features for Action Recognition

Training Deep Convolutional Neural Networks (CNNs) is based on the notion of using multiple kernels and non-linearities in their subsequent activations to extract useful features. The kernels are used as general feature extractors without specific correspondence to the target class. As a result, the extracted features do not correspond to specific classes. Subtle differences between similar classes are modeled in the same way as large differences between dissimilar classes. To overcome the class-agnostic use of kernels in CNNs, we introduce a novel method named Class Regularization that performs class-based regularization of layer activations. We demonstrate that this not only improves feature search during training, but also allows an explicit assignment of features per class during each stage of the feature extraction process. We show that using Class Regularization blocks in state-of-the-art CNN architectures for action recognition leads to systematic improvement gains of 1.8%, 1.2% and 1.4% on the Kinetics, UCF-101 and HMDB-51 datasets, respectively