Addressing the challenge of visual information access from digital image and video libraries

Christel, Michael G.; Conescu, Ronald M.

doi:10.1145/1065385.1065402

Cited by 41 publications

(50 citation statements)

References 21 publications

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“…Virage Video Engine [2] was developed for multimodal indexing and retrieval of videos. Library-based coding [3] is a way of representing images and uses retrieval-enabled MPEG for efficient querying and retrieval.…”

Section: Review Of Literaturementioning

confidence: 99%

Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval

Chadha¹,

Mallik²,

Johar³

2012

IJCA

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The aim of a Content-Based Image Retrieval (CBIR) system, also known as Query by Image Content (QBIC), is to help users to retrieve relevant images based on their contents. CBIR technologies provide a method to find images in large databases by using unique descriptors from a trained image. The image descriptors include texture, color, intensity and shape of the object inside an image. Several feature-extraction techniques viz., Average RGB, Color Moments, Cooccurrence, Local Color Histogram, Global Color Histogram and Geometric Moment have been critically compared in this paper. However, individually these techniques result in poor performance. So, combinations of these techniques have also been evaluated and results for the most efficient combination of techniques have been presented and optimized for each class of image query. We also propose an improvement in image retrieval performance by introducing the idea of Query modification through image cropping. It enables the user to identify a region of interest and modify the initial query to refine and personalize the image retrieval results.

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Section: Review Of Literaturementioning

confidence: 99%

Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval

Chadha¹,

Mallik²,

Johar³

2012

IJCA

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“…• Click on a keyframe to trigger playback of a video shot [2,8] or to perform further actions [3,7]. This feature indicates the users' interest in the video shot which is represented by the keyframe.…”

Section: Implicit Indicatorsmentioning

confidence: 99%

“…Christel and Concescu (2005) [3] developed and compared two video retrieval systems using visual and textual data versus a visual-only system as part of the Informedia project. In addition, they compared expert and naïve users.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the implicit feedback models for adaptive video retrieval

Hopfgartner

Jose

2007

Proceedings of the International Workshop on Workshop on Multimedia Information Retrieval

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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. The use of a simulated evaluation methodology for the comparison of various interactive retrieval strategies has to be explored. 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 relative better performance of an interface with the combination of explicit and implicit features.

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“…The remaining question of validity is whether the subject pool represents a broader set of users, with university students and staff for the most part comprising the subject pool for many research groups because of their availability. Over the years, Informedia TRECVID experiments have confirmed the utility of storyboards showing matching thumbnails across multiple video documents 19 , the differences in expert and novice search behavior when given TRECVID topics 28 , the utility of transcript text for news video topics 29 , and the overlooking of using feature filters (e.g., include or exclude all shots having the face feature or "outdoors" feature) to reduce the shot space 19,28,29 .…”

Section: Trecvid As An Evaluation Forum For Video Summarization and Bmentioning

confidence: 99%

“…Discount usability techniques definitely offer benefit, to both iteratively improve video retrieval interfaces before committing to a more formal empirical study, and also to confirm that changes put in place as a result of a study had their intended effects. HCI guidelines can provide a jump start to the design process, as potentially useful automated video processing can be rendered inaccessible or useless through poor interface implementation 28 .…”

Section: Reflections On Informedia User Study Work and Future Directionsmentioning

confidence: 99%

Evaluation and user studies with respect to video summarization and browsing

Christel

2006

SPIE Proceedings

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The Informedia group at Carnegie Mellon University has since 1994 been developing and evaluating surrogates, summary interfaces, and visualizations for accessing digital video collections containing thousands of documents, millions of shots, and terabytes of data. This paper surveys the Informedia user studies that have taken place through the years, reporting on how these studies can provide a user pull complementing the technology push as automated video processing advances. The merits of discount usability techniques for iterative improvement and evaluation are presented, as well as the structure of formal empirical investigations with end users that have ecological validity while addressing the human computer interaction metrics of efficiency, effectiveness, and satisfaction. The difficulties in evaluating video summarization and browsing interfaces are discussed. Lessons learned from Informedia user studies are reported with respect to video summarization and browsing, ranging from the simplest portrayal of a single thumbnail to represent video stories, to collections of thumbnails in storyboards, to playable video skims, to video collages with multiple synchronized information perspectives.

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Addressing the challenge of visual information access from digital image and video libraries

Cited by 41 publications

References 21 publications

Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval

Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval

Evaluating the implicit feedback models for adaptive video retrieval

Evaluation and user studies with respect to video summarization and browsing

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