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

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

Метод формирования помехоустойчивого кода энергонезависимой памяти приборов семейства jButton типа DS1971 систем санкционированного проезда в лифтах

Рассмотрена задача ограничения доступа в лифты посторонних лиц и пользователей, имеющих существенную задолженность по коммунальным платежам. Предложено использование систем санкционированного проезда, использующих электронные ключи семейства jButton типа DS1971 с энергонезависимой памятью EEPROM. Разработан метод формирования помехоустойчивого кода для энергонезависимой памяти, повышающей безотказность работы лифтовой системы санкционированного проезда.Розглянуто задачу обмеження доступу у ліфти сторонніх осіб та користувачів, що мають суттєву заборгованість по комунальних платежах. Запропоновано використання систем санкціонованого проїзду, які використовують електронні ключі сімейства jButton типу DS1971 з енергонезалежною пам’яттю EEPROM. Розроблено метод формування завадостійкого коду для енергонезалежної пам’яті, що підвищує безвідмовність роботи ліфтової системи санкціонованого проїзду.The problem of restriction of access to lifts of extraneous persons and the users having essential debts on municipal payments is considered. Use of systems of the authorized journey using electronic keys of family jButton of type DS1971 with power independent memory EEPROM is offered. The method of formation of a noiseproof code is developed for the power independent memory, raising non-failure operation of work of lift system of the authorized journey

Mining Bodily Cues to Deception

A significant body of research has investigated potential correlates of deception and bodily behavior. The vast majority of these studies consider discrete, subjectively coded bodily movements such as specific hand or head gestures. Such studies fail to consider quantitative aspects of body movement such as the precise movement direction, magnitude and timing. In this paper, we employ an innovative data mining approach to systematically study bodily correlates of deception. We re-analyze motion capture data from a previously published deception study, and experiment with different data coding options. We report how deception detection rates are affected by variables such as body part, the coding of the pose and movement, the length of the observation, and the amount of measurement noise. Our results demonstrate the feasibility of a data mining approach, with detection rates above 65%, significantly outperforming human judgement (52.80%). Owing to the systematic analysis, our analyses allow for an understanding of the importance of various coding factor. Moreover, we can reconcile seemingly discrepant findings in previous research. Our approach highlights the merits of data-driven research to support the validation and development of deception theory.</p