Robin Aly

Multimedia information seeking through search and hyperlinking

Searching for relevant webpages and following hyperlinks to related content is a widely accepted and effective approach to information seeking on the textual web. Existing work on multimedia information retrieval has focused on search for individual relevant items or on content linking without specific attention to search results. We describe our research exploring integrated multimodal search and hyperlinking for multimedia data. Our investigation is based on the MediaEval 2012 Search and Hyperlinking task. This includes a known-item search task using the Blip10000 internet video collection, where automatically created hyperlinks link each relevant item to related items within the collection. The search test queries and link assessment for this task was generated using the Amazon Mechanical Turk crowdsourcing platform. Our investigation examines a range of alternative methods which seek to address the challenges of search and hyperlinking using multimodal approaches. The results of our experiments are used to propose a research agenda for developing effective techniques for search and hyperlinking of multimedia content.

Taily: shard selection using the tail of score distributions

Search engines can improve their efficiency by selecting only few promising shards for each query. State-of-the-art shard selection algorithms first query a central index of sampled documents, and their effectiveness is similar to searching all shards. However, the search in the central index also hurts efficiency. Additionally, we show that the effectiveness of these approaches varies substantially with the sampled documents. This paper proposes Taily, a novel shard selection algorithm that models a querys score distribution in each shard as a Gamma distribution and selects shards with highly scored documents in the tail of the distribution. Taily estimates the parameters of score distributions based on the mean and variance of the score functions features in the collections and shards. Because Taily operates on term statistics instead of document samples, it is efficient and has deterministic effectiveness. Experiments on large web collections (Gov2, CluewebA and CluewebB) show that Taily achieves similar effectiveness to sample-based approaches, and improves upon their efficiency by roughly 20% in terms of used resources and response time.

Exploiting user disagreement for web search evaluation: an experimental approach

To express a more nuanced notion of relevance as compared to binary judgments, graded relevance levels can be used for the evaluation of search results. Especially in Web search, users strongly prefer top results over less relevant results, and yet they often disagree on which are the top results for a given information need. Whereas previous works have generally considered disagreement as a negative effect, this paper proposes a method to exploit this user disagreement by integrating it into the evaluation procedure. First, we present experiments that investigate the user disagreement. We argue that, with a high disagreement, lower relevance levels might need to be promoted more than in the case where there is global consensus on the top results. This is formalized by introducing the User Disagreement Model, resulting in a weighting of the relevance levels with a probabilistic interpretation. A validity analysis is given, and we explain how to integrate the model with well-established evaluation metrics. Finally, we discuss a specific application of the model, in the estimation of suitable weights for the combined relevance of Web search snippets and pages.

Comparing retrieval effectiveness of alternative content segmentation methods for Internet video search

We present an exploratory study of the retrieval of semiprofessional user-generated Internet video. The study is based on the MediaEval 2011 Rich Speech Retrieval (RSR) task for which the dataset was taken from the Internet sharing platform blip.tv, and search queries associated with specific speech acts occurring in the video. We compare results from three participant groups using: automatic speech recognition system transcript (ASR), metadata manually assigned to each video by the user who uploaded it, and their combination. RSR 2011 was a known-item search for a single manually identified ideal jump-in point in the video for each query where playback should begin. Retrieval effectiveness is measured using the MRR and mGAP metrics. Using different transcript segmentation methods the participants tried to maximize the rank of the relevant item and to locate the nearest match to the ideal jump-in point. Results indicate that best overall results are obtained for topically homogeneous segments which have a strong overlap with the relevant region associated with the jump-in point, and that use of metadata can be beneficial when segments are unfocused or cover more than one topic.

A probabilistic ranking framework using unobservable binary events for video search

Recent content-based video retrieval systems combine output of concept detectors (also known as high-level features) with text obtained through automatic speech recognition. This paper concerns the problem of search using the noisy concept detector output only. Unlike term occurrence in text documents, the event of the occurrence of an audiovisual concept is only indirectly observable. We develop a probabilistic ranking framework for unobservable binary events to search in videos, called PR-FUBE. The framework explicitly models the probability of relevance of a video shot through the presence and absence of concepts. From our framework, we derive a ranking formula and show its relationship to previously proposed formulas. We evaluate our framework against two other retrieval approaches using the TRECVID 2005 and 2007 datasets. Especially using large numbers of concepts in retrieval results in good performance. We attribute the observed robustness against the noise introduced by less related concepts to the effective combination of concept presence and absence in our method. The experiments show that an accurate estimate for the probability of occurrence of a particular concept in relevant shots is crucial to obtain effective retrieval results.

Linking inside a video collection: what and how to measure?

Although linking video to additional information sources seems to be a sensible approach to satisfy information needs of user, the perspective of users is not yet analyzed on a fundamental level in real-life scenarios. However, a better understanding of the motivation of users to follow links in video, which anchors users prefer to link from within a video, and what type of link targets users are typically interested in, is important to be able to model automatic linking of audiovisual content appropriately. In this paper we report on our methodology towards eliciting user requirements with respect to video linking in the course of a broader study on user requirements in searching and a series of benchmark evaluations on searching and linking.

Beyond shot retrieval: searching for broadcast news items using language models of concepts

Current video search systems commonly return video shots as results. We believe that users may better relate to longer, semantic video units and propose a retrieval framework for news story items, which consist of multiple shots. The framework is divided into two parts: (1) A concept based language model which ranks news items with known occurrences of semantic concepts by the probability that an important concept is produced from the concept distribution of the news item and (2) a probabilistic model of the uncertain presence, or risk, of these concepts. In this paper we use a method to evaluate the performance of story retrieval, based on the TRECVID shot-based retrieval groundtruth. Our experiments on the TRECVID 2005 collection show a significant performance improvement against four standard methods.

Simulating the future of concept-based video retrieval under improved detector performance

In this paper we address the following important questions for concept-based video retrieval: (1) What is the impact of detector performance on the performance of concept-based retrieval engines, and (2) will these engines be applicable to real-life search tasks if detector performance improves in the future? We use Monte Carlo simulations to answer these questions. To generate the simulation input, we propose to use a probabilistic model of two Gaussians for the confidence scores that concept detectors emit. Modifying the model’s parameters affects the detector performance and the search performance. We study the relation between these two performances on two video collections. For detectors with similar discriminative power and a concept vocabulary of around 100 concepts, the simulation reveals that in order to achieve a search performance of 0.20 mean average precision (MAP)—which is considered sufficient performance for real-life applications—one needs detectors with at least 0.60 MAP . We also find that, given our simulation model and low detector performance, MAP is not always a good evaluation measure for concept detectors since it is not strongly correlated with the search performance.

Defining and Evaluating Video Hyperlinking for Navigating Multimedia Archives

Multimedia hyperlinking is an emerging research topic in the context of digital libraries and (cultural heritage) archives. We have been studying the concept of video-to-video hyperlinking from a video search perspective in the context of the MediaEval evaluation benchmark for several years. Our task considers a use case of exploring large quantities of video content via an automatically created hyperlink structure at the media fragment level. In this paper we report on our findings, examine the features of the definition of video hyperlinking based on results, and discuss lessons learned with respect to evaluation of hyperlinking in real-life use scenarios.

The AXES PRO video search system

We demonstrate a multimedia content information retrieval engine developed for audiovisual digital libraries targeted at media professionals. It is the first of three multimedia IR systems being developed by the AXES project. The system brings together traditional text IR and state-of-the-art content indexing and retrieval technologies to allow users to search and browse digital libraries in novel ways. Key features include: metadata and ASR search and filtering, on-the-fly visual concept classification (categories, faces, places, and logos), and similarity search (instances and faces).