1,579,902 research outputs found
Associating low-level features with semantic concepts using video objects and relevance feedback
The holy grail of multimedia indexing and retrieval is developing algorithms capable of imitating human abilities in distinguishing and recognising semantic concepts within the content, so that retrieval can be based on âreal worldâ concepts that come naturally to users. In this paper, we discuss an approach to using segmented video objects as the midlevel connection between low-level features and semantic
concept description. In this paper, we consider a video object as a particular instance of a semantic concept and we
model the semantic concept as an average representation
of its instances. A system supporting object-based search
through a test corpus is presented that allows matching presegmented objects based on automatically extracted lowlevel features. In the system, relevance feedback is employed to drive the learning of the semantic model during
a regular search process
Searching for videos on Apple iPad and iPhone
In this demonstration we introduce our content-based video search system which runs as an app on the Apple iPad or iPhone. Our work on video search is motivated by the need to introduce content-based video search techniques, which are currently the preserve of the research community, to the larger YouTube generation. It was with this in mind, that we have developed a simple but engaging content based video search engine which uses an iPad or iPhone app as the front-end user interface. Our app supports the three common modes for content-based video search: text search, concept search and image-similarity search. Our iPad system was evaluated as part of the TRECVid 2010 evaluation campaign where we compared the performance of novice versus expert users
A Dynamical Systems Approach for Static Evaluation in Go
In the paper arguments are given why the concept of static evaluation has the
potential to be a useful extension to Monte Carlo tree search. A new concept of
modeling static evaluation through a dynamical system is introduced and
strengths and weaknesses are discussed. The general suitability of this
approach is demonstrated.Comment: IEEE Transactions on Computational Intelligence and AI in Games, vol
3 (2011), no
Automatic organisation of retrieved images into a hierarchy
Image retrieval is of growing interest to both search engines and academic researchers with increased focus on both content-based and
caption-based approaches. Image search, however, is different from document retrieval: users often search a broader set of retrieved
images than they would examine returned web pages in a search engine. In this paper, we focus on a concept hierarchy generation
approach developed by Sanderson and Croft in 1999, which was used to organise retrieved images in a hierarchy automatically
generated from image captions. Thirty participants were recruited for the study. Each of them conducted two different kinds of
searching tasks within the system. Results indicated that the user retrieval performance in both interfaces of system is similar.
However, the majority of users preferred to use the concept hierarchy to complete their searching tasks and they were satisfied with
using the hierarchical menu to organize retrieved results, because the menu appeared to provide a useful summary to help users look
through the image results
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
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
PrisCrawler: A Relevance Based Crawler for Automated Data Classification from Bulletin Board
Nowadays people realize that it is difficult to find information simply and
quickly on the bulletin boards. In order to solve this problem, people propose
the concept of bulletin board search engine. This paper describes the
priscrawler system, a subsystem of the bulletin board search engine, which can
automatically crawl and add the relevance to the classified attachments of the
bulletin board. Priscrawler utilizes Attachrank algorithm to generate the
relevance between webpages and attachments and then turns bulletin board into
clear classified and associated databases, making the search for attachments
greatly simplified. Moreover, it can effectively reduce the complexity of
pretreatment subsystem and retrieval subsystem and improve the search
precision. We provide experimental results to demonstrate the efficacy of the
priscrawler.Comment: published in GCIS of IEEE WRI '0
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