47,722 research outputs found
Studying Interaction Methodologies in Video Retrieval
So far, several approaches have been studied to bridge the problem of the Semantic Gap, the bottleneck in image and video retrieval. However, no approach is successful enough to increase retrieval performances significantly. One reason is the lack of understanding the user's interest, a major condition towards adapting results to a user. This is partly due to the lack of appropriate interfaces and the missing knowledge of how to interpret user's actions with these interfaces. In this paper, we propose to study the importance of various implicit indicators of relevance. Furthermore, we propose to investigate how this implicit feedback can be combined with static user profiles towards an adaptive video retrieval model
Entity Query Feature Expansion Using Knowledge Base Links
Recent advances in automatic entity linking and knowledge base
construction have resulted in entity annotations for document and
query collections. For example, annotations of entities from large
general purpose knowledge bases, such as Freebase and the Google
Knowledge Graph. Understanding how to leverage these entity
annotations of text to improve ad hoc document retrieval is an open
research area. Query expansion is a commonly used technique to
improve retrieval effectiveness. Most previous query expansion
approaches focus on text, mainly using unigram concepts. In this
paper, we propose a new technique, called entity query feature
expansion (EQFE) which enriches the query with features from
entities and their links to knowledge bases, including structured
attributes and text. We experiment using both explicit query entity
annotations and latent entities. We evaluate our technique on TREC
text collections automatically annotated with knowledge base entity
links, including the Google Freebase Annotations (FACC1) data.
We find that entity-based feature expansion results in significant
improvements in retrieval effectiveness over state-of-the-art text
expansion approaches
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
On the Impact of Entity Linking in Microblog Real-Time Filtering
Microblogging is a model of content sharing in which the temporal locality of
posts with respect to important events, either of foreseeable or unforeseeable
nature, makes applica- tions of real-time filtering of great practical
interest. We propose the use of Entity Linking (EL) in order to improve the
retrieval effectiveness, by enriching the representation of microblog posts and
filtering queries. EL is the process of recognizing in an unstructured text the
mention of relevant entities described in a knowledge base. EL of short pieces
of text is a difficult task, but it is also a scenario in which the information
EL adds to the text can have a substantial impact on the retrieval process. We
implement a start-of-the-art filtering method, based on the best systems from
the TREC Microblog track realtime adhoc retrieval and filtering tasks , and
extend it with a Wikipedia-based EL method. Results show that the use of EL
significantly improves over non-EL based versions of the filtering methods.Comment: 6 pages, 1 figure, 1 table. SAC 2015, Salamanca, Spain - April 13 -
17, 201
User centred evaluation of a recommendation based image browsing system
In this paper, we introduce a novel approach to recommend images by mining user interactions based on implicit feedback of user browsing. The underlying hypothesis is that the interaction implicitly indicates the interests of the users for meeting practical image retrieval tasks. The algorithm mines interaction data and also low-level content of the clicked images to choose diverse images by clustering heterogeneous features. A user-centred, task-oriented, comparative evaluation was undertaken to verify the validity of our approach where two versions of systems { one set up to enable diverse image recommendation { the other allowing browsing only { were compared. Use was made of the two systems by users in simulated work task situations and quantitative and qualitative data collected as indicators of recommendation results and the levels of user's satisfaction. The responses from the users indicate that they nd the more diverse recommendation highly useful
An architecture for life-long user modelling
In this paper, we propose a united architecture for the creation of life-long user profiles. Our architecture combines different steps required for a user prole, including feature extraction and representation, reasoning, recommendation and presentation. We discuss various issues that arise in the context of life-long profiling
Semantic user profiling techniques for personalised multimedia recommendation
Due to the explosion of news materials available through broadcast and other channels, there is an increasing need for personalised news video retrieval. In this work, we introduce a semantic-based user modelling technique to capture users’ evolving information needs. Our approach exploits implicit user interaction to capture long-term user interests in a profile. The organised interests are used to retrieve and recommend news stories to the users. In this paper, we exploit the Linked Open Data Cloud to identify similar news stories that match the users’ interest. We evaluate various recommendation parameters by introducing a simulation-based evaluation scheme
On User Modelling for Personalised News Video Recommendation
In this paper, we introduce a novel approach for modelling user interests. Our approach captures users evolving information needs, identifies aspects of their need and recommends relevant news items to the users. We introduce our approach within the context of personalised news video retrieval. A news video data set is used for experimentation. We employ a simulated user evaluation
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