Beyond Shot Retrieval: Searching for Broadcast News Items Using Language Models of Concepts

Aly, Robin; Hiemstra, Djoerd; Smeaton, Alan F.

doi:10.1007/978-3-642-12275-0_23

Cited by 12 publications

(8 citation statements)

References 19 publications

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“…This is typically not the case in text retrieval, although similar conclusions have been obtained when applying these models to video retrieval (Aly et al 2010). This is consistent with results obtained when graph-based models are applied to recommendation (Clements et al 2009).…”

Section: Discussionsupporting

confidence: 87%

Bridging memory-based collaborative filtering and text retrieval

2012

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription Abstract When speaking of information retrieval, we often mean text retrieval. But there exist many other forms of information retrieval applications. A typical example is collaborative filtering that suggests interesting items to a user by taking into account other users' preferences or tastes. Due to the uniqueness of the problem, it has been modeled and studied differently in the past, mainly drawing from the preference prediction and machine learning view point. A few attempts have yet been made to bring back collaborative filtering to information (text) retrieval modeling and subsequently new interesting collaborative filtering techniques have been thus derived.In this paper, we show that from the algorithmic view point, there is an even closer relationship between collaborative filtering and text retrieval. Specifically, major collaborative filtering algorithms, such as the memory-based, essentially calculate the dot product between the user vector (as the query vector in text retrieval) and the item rating vector (as the document vector in text retrieval). Thus, if we properly structure user preference data and employ the target user's ratings as query input, major text retrieval algorithms and systems can be directly used without any modification. In this regard, we propose a unified formulation under a common notational framework for memory-based collaborative filtering, and a technique to use any text retrieval weighting function with collaborative filtering preference data. Besides confirming the rationale of the framework, our preliminary experimental results have also demonstrated the effectiveness of the approach in using text retrieval models and systems to perform item ranking tasks in collaborative filtering.

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Section: Discussionsupporting

confidence: 87%

Bridging memory-based collaborative filtering and text retrieval

2012

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“…With continuing progress in the development of techniques for automatic concept detection in various domains we have now reached this point of being able to achieve satisfactory results. For example, Aly et al [1] worked in the domain of TV news video where semantic concepts can be detected directly from the video and they developed and tested a probabilistic model which accounts for the uncertain presence of such concepts. This was then evaluated in the application of retrieving news stories from TV news broadcasts.…”

Section: Concept-based Lifelog Retrievalmentioning

confidence: 99%

Using visual lifelogs to automatically characterize everyday activities

Wang

Smeaton

2013

Information Sciences

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Visual lifelogging is the term used to describe recording our everyday lives using wearable cameras, for applications which are personal to us and do not involve sharing our recorded data. Current applications of visual lifelogging are built around remembrance or searching for specific events from the past. The purpose of the work reported here is to extend this to allow us to characterise and measure the occurrence of everyday activities of the wearer and in so doing to gain insights into the wearer's everyday behaviour.The methods we use are to capture everyday activities using a wearable camera called SenseCam, and to use an algorithm we have developed which indexes lifelog images by the occurrence of basic semantic concepts. We then use data reduction techniques to automatically generate a profile of the wearer's everyday behaviour and activities. Our algorithm has been evaluated on a large set of concepts investigated from 13 users in a user experiment, and for a group of 16 popular everyday activities we achieve an * Corresponding author at: CLARITY: Centre for Sensor Web Technologies and School of Computing, Dublin City University, Glasnevin, Dublin 9, Ireland. Tel: +353 -1 -7005262Email address: alan.smeaton@dcu.ie (Alan F. Smeaton) January 12, 2013 average F-score of 0.90. Our conclusions are that the the technique we have presented for unobtrusively and ambiently characterising everyday behaviour and activities across individuals is of sufficient accuracy to be usable in a range of applications. Preprint submitted to Information Sciences

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“…Unlike the majority of approaches that address video retrieval at the shot level (e.g., [15,11]) we consider entire videos as retrieval units. Recently, [1] investigated video retrieval beyond the shot level, but with respect to a task in which relevance was judged based on visual content rather than semantic theme.…”

Section: Related Workmentioning

confidence: 99%

Exploiting noisy visual concept detection to improve spoken content based video retrieval

Rudinac

Larson

Hanjalic

2010

Proceedings of the 18th ACM International Conference on Multimedia

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In this paper, we present a technique for unsupervised construction of concept vectors, concept-based representations of complete video units, from the noisy shot-level output of a set of visual concept detectors. We deploy these vectors to improve spokencontent-based video retrieval using Query Expansion Selection (QES). Our QES approach analyzes results lists returned in response to several alternative query expansions, applying a coherence indicator calculated on top-ranked items to choose the appropriate expansion. The approach is data driven, does not require prior training and relies solely on the analysis of the collection being queried and the results lists produced for the given query text. The experiments, performed on two datasets, TRECVID 2007 and TRECVID 2009, demonstrate the effectiveness of our approach and show that a small set of well-selected visual concept detectors is sufficient to improve retrieval performance.

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Beyond Shot Retrieval: Searching for Broadcast News Items Using Language Models of Concepts

Cited by 12 publications

References 19 publications

Bridging memory-based collaborative filtering and text retrieval

Bridging memory-based collaborative filtering and text retrieval

Using visual lifelogs to automatically characterize everyday activities

Exploiting noisy visual concept detection to improve spoken content based video retrieval

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