Video retrieval using dialogue, keyframe similarity and video objects

There are several different approaches to video retrieval which vary in sophistication, and in the level of their deployment. Some are well-known, others are not yet within our reach for any kind of large volumes of video. In particular, object-based video retrieval, where an object from within a video is used for retrieval, is often particularly desirable from a searcher's perspective. In this paper we introduce Fischlar-Simpsons, a system providing retrieval from an archive of video using any combination of text searching, keyframe image matching, shot-level browsing, as well as object-based retrieval. The system is driven by user feedback and interaction rather than having the conventional search/browse/search metaphor and the purpose of the system is to explore how users can use detected objects in a shot as part of a retrieval task

Simulated evaluation of faceted browsing based on feature selection

In this paper we explore the limitations of facet based browsing which uses sub-needs of an information need for querying and organising the search process in video retrieval. The underlying assumption of this approach is that the search effectiveness will be enhanced if such an approach is employed for interactive video retrieval using textual and visual features. We explore the performance bounds of a faceted system by carrying out a simulated user evaluation on TRECVid data sets, and also on the logs of a prior user experiment with the system. We first present a methodology to reduce the dimensionality of features by selecting the most important ones. Then, we discuss the simulated evaluation strategies employed in our evaluation and the effect on the use of both textual and visual features. Facets created by users are simulated by clustering video shots using textual and visual features. The experimental results of our study demonstrate that the faceted browser can potentially improve the search effectiveness

Using video objects and relevance feedback in video retrieval

Video retrieval is mostly based on using text from dialogue and this remains the most signi¯cant component, despite progress in other aspects. One problem with this is when a searcher wants to locate video based on what is appearing in the video rather than what is being spoken about. Alternatives such as automatically-detected features and image-based keyframe matching can be used, though these still need further improvement in quality. One other modality for video retrieval is based on segmenting objects from video and allowing end users to use these as part of querying. This uses similarity between query objects and objects from video, and in theory allows retrieval based on what is actually appearing on-screen. The main hurdles to greater use of this are the overhead of object segmentation on large amounts of video and the issue of whether we can actually achieve effective object-based retrieval. We describe a system to support object-based video retrieval where a user selects example video objects as part of the query. During a search a user builds up a set of these which are matched against objects previously segmented from a video library. This match is based on MPEG-7 Dominant Colour, Shape Compaction and Texture Browsing descriptors. We use a user-driven semi-automated segmentation process to segment the video archive which is very accurate and is faster than conventional video annotation

Improving instance search performance in video collections

This thesis presents methods to improve instance search and enhance user performance while browsing unstructured video collections. Through the use of computer vision and information retrieval techniques, we propose novel solutions to analyse visual content and build a search algorithm to address the challenges of visual instance search, while considering the constraints for practical applications. Firstly, we investigate methods to improve the effectiveness of instance search systems for finding object instances which occurred in unstructured video content. Using the bag of feature framework, we propose a novel algorithm to use the geometric correlation information between local features to improve the accuracy of local feature matching, thus improve the performance of instance search systems without introducing much computation cost. Secondly, we consider the scenario that the performance of instance search systems may drop due to the volume of visual content in large video collections. We introduce a search algorithm based on embedded coding to increase the effectiveness and efficiency of instance search systems. And we participate in the international video evaluation campaign, TREC Video Retrieval Evaluation, to comparatively evaluate the performance of our proposed methods. Finally, the exploration and navigation of visual content when browsing large unstructured video collections is considered. We propose methods to address such challenges and build an interactive video browsing tool to improve user performance while seeking interesting content over video collections. We construct a structured content representation with similarity graph using our proposed instance search technologies. Considering the constraints related to real world usability, we present a flexible interface based on faceted navigation to enhance user performance when completing video browsing tasks. This thesis shows that user performance can be enhanced by improving the effectiveness of instance search approaches, when seeking information in unstructured video collection. While covering many different aspects of improving instance search in this work, we outline three potential directions for future work: advanced feature representation, data driven rank and cloud-based search algorithms

Video browsing interfaces and applications: a review

We present a comprehensive review of the state of the art in video browsing and retrieval systems, with special emphasis on interfaces and applications. There has been a significant increase in activity (e.g., storage, retrieval, and sharing) employing video data in the past decade, both for personal and professional use. The ever-growing amount of video content available for human consumption and the inherent characteristics of video data—which, if presented in its raw format, is rather unwieldy and costly—have become driving forces for the development of more effective solutions to present video contents and allow rich user interaction. As a result, there are many contemporary research efforts toward developing better video browsing solutions, which we summarize. We review more than 40 different video browsing and retrieval interfaces and classify them into three groups: applications that use video-player-like interaction, video retrieval applications, and browsing solutions based on video surrogates. For each category, we present a summary of existing work, highlight the technical aspects of each solution, and compare them against each other

Interactive searching and browsing of video archives: using text and using image matching

Over the last number of decades much research work has been done in the general area of video and audio analysis. Initially the applications driving this included capturing video in digital form and then being able to store, transmit and render it, which involved a large effort to develop compression and encoding standards. The technology needed to do all this is now easily available and cheap, with applications of digital video processing now commonplace, ranging from CCTV (Closed Circuit TV) for security, to home capture of broadcast TV on home DVRs for personal viewing. One consequence of the development in technology for creating, storing and distributing digital video is that there has been a huge increase in the volume of digital video, and this in turn has created a need for techniques to allow effective management of this video, and by that we mean content management. In the BBC, for example, the archives department receives approximately 500,000 queries per year and has over 350,000 hours of content in its library. Having huge archives of video information is hardly any benefit if we have no effective means of being able to locate video clips which are of relevance to whatever our information needs may be. In this chapter we report our work on developing two specific retrieval and browsing tools for digital video information. Both of these are based on an analysis of the captured video for the purpose of automatically structuring into shots or higher level semantic units like TV news stories. Some also include analysis of the video for the automatic detection of features such as the presence or absence of faces. Both include some elements of searching, where a user specifies a query or information need, and browsing, where a user is allowed to browse through sets of retrieved video shots. We support the presentation of these tools with illustrations of actual video retrieval systems developed and working on hundreds of hours of video content

A system for event-based film browsing

The recent past has seen a proliferation in the amount of digital video content being created and consumed. This is perhaps being driven by the increase in audiovisual quality, as well as the ease with which production, reproduction and consumption is now possible. The widespread use of digital video, as opposed its analogue counterpart, has opened up a plethora of previously impossible applications. This paper builds upon previous work that analysed digital video, namely movies, in order to facilitate presentation in an easily navigable manner. A film browsing interface, termed the MovieBrowser, is described, which allows users to easily locate specific portions of movies, as well as to obtain an understanding of the filming being perused. A number of experiments which assess the system’s performance are also presented

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