216 research outputs found
Dublin City University at CLEF 2006: Experiments for the ImageCLEF Photo Collection Standard Ad Hoc Task
We provide a technical description of our submission to the CLEF 2006 Cross Language Image Retrieval(ImageCLEF) Photo Collection Standard Ad Hoc task. We performed monolingual and cross language retrieval of photo images using photo annotations with and without feedback, and also a combined visual and text retrieval approach. Topics are translated into English using the Babelfish online machine translation
system. Our text runs used the BM25 algorithm, while our visual approach used simple low-level features with matching based on the Jeffrey Divergence measure. Our results consistently indicate that the fusion of text and visual features is best for this task, and that performing feedback for text consistently improves on the baseline
non-feedback BM25 text runs for all language pairs
Dublin City University at TRECVID 2008
In this paper we describe our system and experiments performed for both the automatic search task and the event detection task in TRECVid 2008. For the automatic search task for 2008 we submitted 3 runs utilizing only visual retrieval experts, continuing our previous work in examining techniques for query-time weight generation for data-fusion and determining what we can get from global visual only experts. For the event detection task we submitted results for 5 required events (ElevatorNoEntry, OpposingFlow, PeopleMeet, Embrace and PersonRuns) and 1 optional event (DoorOpenClose)
Online Forum Thread Retrieval using Pseudo Cluster Selection and Voting Techniques
Online forums facilitate knowledge seeking and sharing on the Web. However,
the shared knowledge is not fully utilized due to information overload. Thread
retrieval is one method to overcome information overload. In this paper, we
propose a model that combines two existing approaches: the Pseudo Cluster
Selection and the Voting Techniques. In both, a retrieval system first scores a
list of messages and then ranks threads by aggregating their scored messages.
They differ on what and how to aggregate. The pseudo cluster selection focuses
on input, while voting techniques focus on the aggregation method. Our combined
models focus on the input and the aggregation methods. The result shows that
some combined models are statistically superior to baseline methods.Comment: The original publication is available at
http://www.springerlink.com/. arXiv admin note: substantial text overlap with
arXiv:1212.533
Combination of content analysis and context features for digital photograph retrieval.
In recent years digital cameras have seen an enormous rise
in popularity, leading to a huge increase in the quantity of
digital photos being taken. This brings with it the challenge of organising these large collections. The MediAssist project uses date/time and GPS location for the
organisation of personal collections. However, this context
information is not always sufficient to support retrieval
when faced with a large, shared, archive made up of
photos from a number of users. We present work in this
paper which retrieves photos of known objects (buildings,
monuments) using both location information and content-based
retrieval tools from the AceToolbox. We show that
for this retrieval scenario, where a user is searching for
photos of a known building or monument in a large shared
collection, content-based techniques can offer a significant
improvement over ranking based on context (specifically
location) alone
TRECVid 2006 experiments at Dublin City University
In this paper we describe our retrieval system and experiments performed for the automatic search task in TRECVid 2006. We submitted the following six automatic runs:
⢠F A 1 DCU-Base 6: Baseline run using only ASR/MT text features.
⢠F A 2 DCU-TextVisual 2: Run using text and visual features.
⢠F A 2 DCU-TextVisMotion 5: Run using text, visual, and motion features.
⢠F B 2 DCU-Visual-LSCOM 3: Text and visual features combined with concept detectors.
⢠F B 2 DCU-LSCOM-Filters 4: Text, visual, and motion features with concept detectors.
⢠F B 2 DCU-LSCOM-2 1: Text, visual, motion, and concept detectors with negative concepts.
The experiments were designed both to study the addition of motion features and separately constructed models for semantic concepts, to runs using only textual and visual features, as well as to establish a baseline for the manually-assisted search runs performed within the collaborative K-Space project and described in the corresponding TRECVid 2006 notebook paper. The results of
the experiments indicate that the performance of automatic search can be improved with suitable concept models. This, however, is very topic-dependent and the questions of when to include such models and which concept models should be included, remain unanswered. Secondly, using motion features did not lead to performance improvement in our experiments. Finally, it was observed that our text features, despite displaying a rather poor performance overall, may still be useful even for generic search topics
A comparison of score, rank and probability-based fusion methods for video shot retrieval
It is now accepted that the most effective video shot retrieval is based on indexing and retrieving clips using multiple, parallel modalities such as text-matching, image-matching and feature matching and then combining or fusing these parallel retrieval streams in some way. In this paper we investigate a range of fusion methods for combining based on multiple visual features (colour, edge and texture), for combining based on multiple visual examples in the query and for combining multiple modalities (text and visual). Using three TRECVid collections and the TRECVid search task, we specifically compare fusion methods based on normalised score and rank that use either the average, weighted average or maximum of retrieval results from a discrete Jelinek-Mercer smoothed language model. We also compare these results with a simple probability-based combination of the language model results that assumes all features and visual examples are fully independent
Assessing the Effectiveness and Usability of Personalized Internet Search through a Longitudinal Evaluation
This paper discusses a longitudinal user evaluation of Prospector, a personalized Internet meta-search engine capable of personalized re-ranking of search results. Twenty-one participants used Prospector as their primary search engine for 12 days, agreed to have their interaction with the system logged, and completed three questionnaires. The data logs show that the personalization provided by Prospector is successful: participants preferred re-ranked results that appeared higher up. However, the questionnaire results indicated that people would prefer to use Google instead (their search engine of choice). Users would, nevertheless, consider employing a personalized search engine to perform searches with terms that require disambiguation and/or contextualization. We conclude the paper with a discussion on the merit of combining system- and user-centered evaluation for the case of personalized systems
AXES at TRECVid 2011
The AXES project participated in the interactive known-item search task (KIS) and the interactive instance search task (INS) for TRECVid 2011. We used the same system architecture and a nearly identical user interface for both the KIS and INS tasks. Both systems made use of text search on ASR, visual concept detectors, and visual similarity search. The user experiments were carried out with media professionals and media students at the Netherlands Institute for Sound and Vision, with media professionals performing the KIS task and media students participating in the INS task. This paper describes the results and findings of our experiments
- âŚ