With massive amounts of data producing each day in almost every field, traditional data processing techniques have become more and more inadequate. However, the research of effectively managing and re...
Wissen und Informationen wachsen nicht nur stetig in ihrer Menge, sie stellen heute vielmehr eine bedeutende Ressource vieler Unternehmen dar. Der effiziente Zugriff auf Unternehmenswissen, wie etwa E...
When witnessing the great increase of video data available, it becomes clear that summarization is one of the great challenges ahead. One particular problem is the summarization of video rushes. In t...
When we say a person is texting, can you tell the person is walking or sitting? Emphatically, no. In order to solve this incomplete representation problem, this paper presents a sub-action descriptor ...
When video collections become huge, how to explore both within and across videos efficiently is challenging. Video summarization is one of the ways to tackle this issue. Traditional summarization appr...
When considering multimedia database growth, one current challenging issue is to design accurate navigation tools. End user basic needs, such as exploration, similarity search and favorite suggestions...
What we learned from our runs: using a commercial facedetection package without tweaking on this (low image quality) dataset does not work. Using a small-sized (512 words) visual vocabulary computed o...
Web videos available in sharing sites like YouTube, are becoming an alternative to manually annotated training data, which are necessary for creating video classifiers. However, when looking into web ...
Web video categorization is one of the emerging research fields in the computer vision domain due to its massive volume growth in the internet which demands to discover events. Due to insufficient, no...
We study the usefulness of intermediate semantic concepts in bridging the semantic gap in automatic video retrieval. The results of a series of large-scale retrieval experiments, which combine text-ba...
We study the problem of object recognition for categories for which we have no training examples, a task also called zero--data or zero-shot learning. This situation has hardly been studied in compute...
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...
We studied a method using support vector machines (SVMs) with walk-based graph kernels for the high-level feature extraction (HLF) task. In this method, each image is first segmented into a finite set...
We start from the state-of-the-art Bag of Words pipeline that in the 2008 benchmarks of TRECvid and PASCAL yielded the best performance scores. We have contributed to that pipeline, which now forms th...
We show that a class of nonlinear kernel SVMs admits approximate classifiers with runtime and memory complexity that is independent of the number of support vectors. This class of kernels, which we re...
We report results on audio copy detection for TRECVID 2009 copy detection task. This task involves searching for transformed audio queries in over 385 h of test audio. The queries were transformed in ...
We report results on audio copy detection for TRECVID 2009 copy detection task. This task involves searching for transformed audio queries in over 385 hours of test audio. The queries were transformed...
We report on our system used in the TRECVID 2013 Multimedia Event Detection (MED) and Multimedia Event Recounting (MER) tasks. For MED, it consists of four main steps: extracting features, representin...
We provide a survey of the field of Music Information Retrieval (MIR), in particular paying attention to latest developments, such as semantic auto-tagging and user-centric retrieval and recommendatio...
We provide a benchmark for digital Media Forensics Challenge (MFC) evaluations. Our comprehensive data comprises over 176,000 high provenance (HP) images and 11,000 HP videos; more than 100,000 manipu...