TRECVid 2011 Experiments at Dublin City University

This year the iAd-DCU team participated in three of the assigned TRECVid 2011 tasks; Semantic Indexing (SIN), Interactive Known-Item Search (KIS) and Multimedia Event Detection (MED). For the SIN task we presented three full runs using global features, local features and fusion of global, local features and relationships between concepts respectively. The evaluation results show that local features achieve better performance, with marginal gains found when introducing global features and relationships between concepts. With regard to our KIS submission, similar to our 2010 KIS experiments, we have implemented an iPad interface to a KIS video search tool. The aim of this year’s experimentation was to evaluate different display methodologies for KIS interaction. For this work, we integrate a clustering element for keyframes, which operates over MPEG-7 features using k-means clustering. In addition, we employ concept detection, not simply for search, but as a means of choosing most representative keyframes for ranked items. For our experiments we compare the baseline non-clustering system to a clustering system on a topic by topic basis. Finally, for the first time this year the iAd group at DCU has been involved in the MED Task. Two techniques are compared, employing low-level features directly and using concepts as intermediate representations. Evaluation results show promising initial results when performing event detection using concepts as intermediate representations

High-level feature detection from video in TRECVid: a 5-year retrospective of achievements

Successful and effective content-based access to digital video requires fast, accurate and scalable methods to determine the video content automatically. A variety of contemporary approaches to this rely on text taken from speech within the video, or on matching one video frame against others using low-level characteristics like colour, texture, or shapes, or on determining and matching objects appearing within the video. Possibly the most important technique, however, is one which determines the presence or absence of a high-level or semantic feature, within a video clip or shot. By utilizing dozens, hundreds or even thousands of such semantic features we can support many kinds of content-based video navigation. Critically however, this depends on being able to determine whether each feature is or is not present in a video clip. The last 5 years have seen much progress in the development of techniques to determine the presence of semantic features within video. This progress can be tracked in the annual TRECVid benchmarking activity where dozens of research groups measure the effectiveness of their techniques on common data and using an open, metrics-based approach. In this chapter we summarise the work done on the TRECVid high-level feature task, showing the progress made year-on-year. This provides a fairly comprehensive statement on where the state-of-the-art is regarding this important task, not just for one research group or for one approach, but across the spectrum. We then use this past and on-going work as a basis for highlighting the trends that are emerging in this area, and the questions which remain to be addressed before we can achieve large-scale, fast and reliable high-level feature detection on video

VITALAS at TRECVID-2008

In this paper, we present our experiments in TRECVID 2008 about High-Level feature extraction task. This is the first year for our participation in TRECVID, our system adopts some popular approaches that other workgroups proposed before. We proposed 2 advanced low-level features NEW Gabor texture descriptor and the Compact-SIFT Codeword histogram. Our system applied well-known LIBSVM to train the SVM classifier for the basic classifier. In fusion step, some methods were employed such as the Voting, SVM-base, HCRF and Bootstrap Average AdaBoost(BAAB)

K-Space at TRECVid 2007

In this paper we describe K-Space participation in TRECVid 2007. K-Space participated in two tasks, high-level feature extraction and interactive search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission utilized multi-modal low-level features which included visual, audio and temporal elements. Specific concept detectors (such as Face detectors) developed by K-Space partners were also used. We experimented with different machine learning approaches including logistic regression and support vector machines (SVM). Finally we also experimented with both early and late fusion for feature combination. This year we also participated in interactive search, submitting 6 runs. We developed two interfaces which both utilized the same retrieval functionality. Our objective was to measure the effect of context, which was supported to different degrees in each interface, on user performance. The first of the two systems was a ‘shot’ based interface, where the results from a query were presented as a ranked list of shots. The second interface was ‘broadcast’ based, where results were presented as a ranked list of broadcasts. Both systems made use of the outputs of our high-level feature submission as well as low-level visual features

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

K-Space at TRECVID 2008

In this paper we describe K-Space’s participation in TRECVid 2008 in the interactive search task. For 2008 the K-Space group performed one of the largest interactive video information retrieval experiments conducted in a laboratory setting. We had three institutions participating in a multi-site multi-system experiment. In total 36 users participated, 12 each from Dublin City University (DCU, Ireland), University of Glasgow (GU, Scotland) and Centrum Wiskunde and Informatica (CWI, the Netherlands). Three user interfaces were developed, two from DCU which were also used in 2007 as well as an interface from GU. All interfaces leveraged the same search service. Using a latin squares arrangement, each user conducted 12 topics, leading in total to 6 runs per site, 18 in total. We officially submitted for evaluation 3 of these runs to NIST with an additional expert run using a 4th system. Our submitted runs performed around the median. In this paper we will present an overview of the search system utilized, the experimental setup and a preliminary analysis of our results

K-Space at TRECVid 2008

In this paper we describe K-Space’s participation in TRECVid 2008 in the interactive search task. For 2008 the K-Space group performed one of the largest interactive video information retrieval experiments conducted in a laboratory setting. We had three institutions participating in a multi-site multi-system experiment. In total 36 users participated, 12 each from Dublin City University (DCU, Ireland), University of Glasgow (GU, Scotland) and Centrum Wiskunde & Informatica (CWI, the Netherlands). Three user interfaces were developed, two from DCU which were also used in 2007 as well as an interface from GU. All interfaces leveraged the same search service. Using a latin squares arrangement, each user conducted 12 topics, leading in total to 6 runs per site, 18 in total. We officially submitted for evaluation 3 of these runs to NIST with an additional expert run using a 4th system. Our submitted runs performed around the median. In this paper we will present an overview of the search system utilized, the experimental setup and a preliminary analysis of our results

Query generation from multiple media examples

This paper exploits an unified media document representation called feature terms for query generation from multiple media examples, e.g. images. A feature term refers to a value interval of a media feature. A media document is therefore represented by a frequency vector about feature term appearance. This approach (1) facilitates feature accumulation from multiple examples; (2) enables the exploration of text-based retrieval models for multimedia retrieval. Three statistical criteria, minimised chi-squared, minimised AC/DC rate and maximised entropy, are proposed to extract feature terms from a given media document collection. Two textual ranking functions, KL divergence and a BM25-like retrieval model, are adapted to estimate media document relevance. Experiments on the Corel photo collection and the TRECVid 2006 collection show the effectiveness of feature term based query in image and video retrieval

The Lowlands team at TRECVID 2008

In this paper we describe our experiments performed for TRECVID 2008. We participated in the High Level Feature extraction and the Search task. For the High Level Feature extraction task we mainly installed our detection environment. In the Search task we applied our new PRFUBE ranking model together with an estimation method which estimates a vital parameter of the model, the probability of a concept occurring in relevant shots. The PRFUBE model has similarities to the well known Probabilistic Text Information Retrieval methodology and follows the Probability Ranking Principle

TRECVid 2007 experiments at Dublin City University

In this paper we describe our retrieval system and experiments performed for the automatic search task in TRECVid 2007. We submitted the following six automatic runs: • F A 1 DCU-TextOnly6: Baseline run using only ASR/MT text features. • F A 1 DCU-ImgBaseline4: Baseline visual expert only run, no ASR/MT used. Made use of query-time generation of retrieval expert coefficients for fusion. • F A 2 DCU-ImgOnlyEnt5: Automatic generation of retrieval expert coefficients for fusion at index time. • F A 2 DCU-imgOnlyEntHigh3: Combination of coefficient generation which combined the coefficients generated by the query-time approach, and the index-time approach, with greater weight given to the index-time coefficient. • F A 2 DCU-imgOnlyEntAuto2: As above, except that greater weight is given to the query-time coefficient that was generated. • F A 2 DCU-autoMixed1: Query-time expert coefficient generation that used both visual and text experts