The Mirror DBMS at TREC-8

The database group at University of Twente participates in TREC8 using the Mirror DBMS, a prototype database system especially designed for multimedia and web retrieval. From a database perspective, the purpose has been to check whether we can get sufficient performance, and to prepare for the very large corpus track in which we plan to participate next year. From an IR perspective, the experiments have been designed to learn more about the effect of the global statistics on the ranking

User Feedback in Probabilistic XML

Data integration is a challenging problem in many application areas. Approaches mostly attempt to resolve semantic uncertainty and conflicts between information sources as part of the data integration process. In some application areas, this is impractical or even prohibitive, for example, in an ambient environment where devices on an ad hoc basis have to exchange information autonomously. We have proposed a probabilistic XML approach that allows data integration without user involvement by storing semantic uncertainty and conflicts in the integrated XML data. As a\ud consequence, the integrated information source represents\ud all possible appearances of objects in the real world, the\ud so-called possible worlds.\ud \ud In this paper, we show how user feedback on query results\ud can resolve semantic uncertainty and conflicts in the\ud integrated data. Hence, user involvement is effectively postponed to query time, when a user is already interacting actively with the system. The technique relates positive and\ud negative statements on query answers to the possible worlds\ud of the information source thereby either reinforcing, penalizing, or eliminating possible worlds. We show that after repeated user feedback, an integrated information source better resembles the real world and may converge towards a non-probabilistic information source

Distinguishing Posed and Spontaneous Smiles by Facial Dynamics

Smile is one of the key elements in identifying emotions and present state of mind of an individual. In this work, we propose a cluster of approaches to classify posed and spontaneous smiles using deep convolutional neural network (CNN) face features, local phase quantization (LPQ), dense optical flow and histogram of gradient (HOG). Eulerian Video Magnification (EVM) is used for micro-expression smile amplification along with three normalization procedures for distinguishing posed and spontaneous smiles. Although the deep CNN face model is trained with large number of face images, HOG features outperforms this model for overall face smile classification task. Using EVM to amplify micro-expressions did not have a significant impact on classification accuracy, while the normalizing facial features improved classification accuracy. Unlike many manual or semi-automatic methodologies, our approach aims to automatically classify all smiles into either `spontaneous' or `posed' categories, by using support vector machines (SVM). Experimental results on large UvA-NEMO smile database show promising results as compared to other relevant methods.Comment: 16 pages, 8 figures, ACCV 2016, Second Workshop on Spontaneous Facial Behavior Analysi

Side-View Face Recognition

Side-view face recognition is a challenging problem with many applications. Especially in real-life scenarios where the environment is uncontrolled, coping with pose variations up to side-view positions is an important task for face recognition. In this paper we discuss the use of side view face recognition techniques to be used in house safety applications. Our aim is to recognize people as they pass through a door, and estimate their location in the house. Here, we compare available databases appropriate for this task, and review current methods for profile face recognition

Scale Selective Extended Local Binary Pattern for Texture Classification

In this paper, we propose a new texture descriptor, scale selective extended local binary pattern (SSELBP), to characterize texture images with scale variations. We first utilize multi-scale extended local binary patterns (ELBP) with rotation-invariant and uniform mappings to capture robust local micro- and macro-features. Then, we build a scale space using Gaussian filters and calculate the histogram of multi-scale ELBPs for the image at each scale. Finally, we select the maximum values from the corresponding bins of multi-scale ELBP histograms at different scales as scale-invariant features. A comprehensive evaluation on public texture databases (KTH-TIPS and UMD) shows that the proposed SSELBP has high accuracy comparable to state-of-the-art texture descriptors on gray-scale-, rotation-, and scale-invariant texture classification but uses only one-third of the feature dimension.Comment: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 201