AXES at TRECVID 2012: KIS, INS, and MED

The AXES project participated in the interactive instance search task (INS), the known-item search task (KIS), and the multimedia event detection task (MED) for TRECVid 2012. As in our TRECVid 2011 system, we used nearly identical search systems and user interfaces for both INS and KIS. Our interactive INS and KIS systems focused this year on using classifiers trained at query time with positive examples collected from external search engines. Participants in our KIS experiments were media professionals from the BBC; our INS experiments were carried out by students and researchers at Dublin City University. We performed comparatively well in both experiments. Our best KIS run found 13 of the 25 topics, and our best INS runs outperformed all other submitted runs in terms of P@100. For MED, the system presented was based on a minimal number of low-level descriptors, which we chose to be as large as computationally feasible. These descriptors are aggregated to produce high-dimensional video-level signatures, which are used to train a set of linear classifiers. Our MED system achieved the second-best score of all submitted runs in the main track, and best score in the ad-hoc track, suggesting that a simple system based on state-of-the-art low-level descriptors can give relatively high performance. This paper describes in detail our KIS, INS, and MED systems and the results and findings of our experiments

Search and hyperlinking task at MediaEval 2012

The Search and Hyperlinking Task was one of the Brave New Tasks at MediaEval 2012. The Task consisted of two subtasks which focused on search and linking in retrieval from a collection of semi-professional video content. These tasks followed up on research carried out within the MediaEval 2011 Rich Speech Retrieval (RSR) Task and the VideoCLEF 2009 Linking Task

Unified Embedding and Metric Learning for Zero-Exemplar Event Detection

Event detection in unconstrained videos is conceived as a content-based video retrieval with two modalities: textual and visual. Given a text describing a novel event, the goal is to rank related videos accordingly. This task is zero-exemplar, no video examples are given to the novel event. Related works train a bank of concept detectors on external data sources. These detectors predict confidence scores for test videos, which are ranked and retrieved accordingly. In contrast, we learn a joint space in which the visual and textual representations are embedded. The space casts a novel event as a probability of pre-defined events. Also, it learns to measure the distance between an event and its related videos. Our model is trained end-to-end on publicly available EventNet. When applied to TRECVID Multimedia Event Detection dataset, it outperforms the state-of-the-art by a considerable margin.Comment: IEEE CVPR 201

TRECVID 2014 -- An Overview of the Goals, Tasks, Data, Evaluation Mechanisms and Metrics

International audienceThe TREC Video Retrieval Evaluation (TRECVID) 2014 was a TREC-style video analysis and retrieval evaluation, the goal of which remains to promote progress in content-based exploitation of digital video via open, metrics-based evaluation. Over the last dozen years this effort has yielded a better under- standing of how systems can effectively accomplish such processing and how one can reliably benchmark their performance. TRECVID is funded by the NIST with support from other US government agencies. Many organizations and individuals worldwide contribute significant time and effort

Learning to detect video events from zero or very few video examples

In this work we deal with the problem of high-level event detection in video. Specifically, we study the challenging problems of i) learning to detect video events from solely a textual description of the event, without using any positive video examples, and ii) additionally exploiting very few positive training samples together with a small number of ``related'' videos. For learning only from an event's textual description, we first identify a general learning framework and then study the impact of different design choices for various stages of this framework. For additionally learning from example videos, when true positive training samples are scarce, we employ an extension of the Support Vector Machine that allows us to exploit ``related'' event videos by automatically introducing different weights for subsets of the videos in the overall training set. Experimental evaluations performed on the large-scale TRECVID MED 2014 video dataset provide insight on the effectiveness of the proposed methods.Comment: Image and Vision Computing Journal, Elsevier, 2015, accepted for publicatio

Video Stream Retrieval of Unseen Queries using Semantic Memory

Retrieval of live, user-broadcast video streams is an under-addressed and increasingly relevant challenge. The on-line nature of the problem requires temporal evaluation and the unforeseeable scope of potential queries motivates an approach which can accommodate arbitrary search queries. To account for the breadth of possible queries, we adopt a no-example approach to query retrieval, which uses a query's semantic relatedness to pre-trained concept classifiers. To adapt to shifting video content, we propose memory pooling and memory welling methods that favor recent information over long past content. We identify two stream retrieval tasks, instantaneous retrieval at any particular time and continuous retrieval over a prolonged duration, and propose means for evaluating them. Three large scale video datasets are adapted to the challenge of stream retrieval. We report results for our search methods on the new stream retrieval tasks, as well as demonstrate their efficacy in a traditional, non-streaming video task.Comment: Presented at BMVC 2016, British Machine Vision Conference, 201

Pursuing a moving target: iterative use of benchmarking of a task to understand the task

Individual tasks carried out within benchmarking initiatives, or campaigns, enable direct comparison of alternative approaches to tackling shared research challenges and ideally promote new research ideas and foster communities of researchers interested in common or related scientific topics. When a task has a clear predefined use case, it might straightforwardly adopt a well established framework and methodology. For example, an ad hoc information retrieval task adopting the standard Cranfield paradigm. On the other hand, in cases of new and emerging tasks which pose more complex challenges in terms of use scenarios or dataset design, the development of a new task is far from a straightforward process. This letter summarises our reflections on our experiences as task organisers of the Search and Hyperlinking task from its origins as a Brave New Task at the MediaEval benchmarking campaign (2011–2014) to its current instantiation as a task at the NIST TRECVid benchmark (since 2015). We highlight the challenges encountered in the development of the task over a number of annual iterations, the solutions found so far, and our process for maintaining a vision for the ongoing advancement of the task’s ambition