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

Evaluating the implicit feedback models for adaptive video retrieval

Interactive video retrieval systems are becoming popular. On the one hand, these systems try to reduce the effect of the semantic gap, an issue currently being addressed by the multimedia retrieval community. On the other hand, such systems enhance the quality of information seeking for the user by supporting query formulation and reformulation. Interactive systems are very popular in the textual retrieval domain. However, they are relatively unexplored in the case of multimedia retrieval. The main problem in the development of interactive retrieval systems is the evaluation cost.The traditional evaluation methodology, as used in the information retrieval domain, is not applicable. An alternative is to use a user-centred evaluation methodology. However, such schemes are expensive in terms of effort, cost and are not scalable. This problem gets exacerbated by the use of implicit indicators, which are useful and increasingly used in predicting user intentions. In this paper, we explore the effectiveness of a number of interfaces and feedback mechanisms and compare their relative performance using a simulated evaluation methodology. The results show the relatively better performance of a search interface with the combination of explicit and implicit features

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

A news video retrieval framework for the study of implicit relevance feedback.

In this paper, we propose a framework for recording, analysing, indexing and retrieving news videos such as the BBC one o'clock news. We believe that such a framework will be useful to identify implicit indicators of relevance, a nearly untouched area in adaptive multimedia retrieval. Due to its advantages as a Web application and its up-to-date content, it can be a promising approach to motivate a broad quantity of users to interact with the system

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

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 Adaptive News Video Retrieval Framework

The increasing popularity of video sharing platforms such as YouTube and Google Video increase the need to further study how users can be assisted in their search for videos they are interested in. In this demo, we present a video retrieval system which guarantees the user easy and effective access to a large news video collection. This system can be used to further study interaction methodologies, aiming for a personalised video retrieval model which adapts retrieval results to the user's interests

Implicit search trails for video recommendation

In this demo paper we demonstrate our approach and system for using implicit actions involved in video search to provide recommendations to users. The goal of this system is to improve the quality of the results that users find, and in doing so, help users to explore a large and difficult information space and help them consider search options that they may not have considered otherwise. Results of a user evaluation show that this approach achieves all of these goals

Simulated testing of an adaptive multimedia information retrieval system

The Semantic Gap is considered to be a bottleneck in image and video retrieval. One way to increase the communication between user and system is to take advantage of the user's action with a system, e.g. to infer the relevance or otherwise of a video shot viewed by the user. In this paper we introduce a novel video retrieval system and propose a model of implicit information for interpreting the user's actions with the interface. The assumptions on which this model was created are then analysed in an experiment using simulated users based on relevance judgements to compare results of explicit and implicit retrieval cycles. Our model seems to enhance retrieval results. Results are presented and discussed in the final section

Collaborative search trails for video search

In this paper we present an approach for supporting users in the difficult task of searching for video. We use collaborative feedback mined from the interactions of earlier users of a video search system to help users in their current search tasks. Our objective is to improve the quality of the results that users find, and in doing so also assist users to explore a large and complex information space. It is hoped that this will lead to them considering search options that they may not have considered otherwise. We performed a user centred evaluation. The results of our evaluation indicate that we achieved our goals, the performance of the users in finding relevant video clips was enhanced with our system; users were able to explore the collection of video clips more and users demonstrated a preference for our system that provided recommendations