Establishing the utility of non-text search for news video retrieval with real world users

TRECVID participants have enjoyed consistent success using storyboard interfaces for shot-based retrieval, as measured by TRECVID interactive search mean average precision (MAP). However, much is lost by only looking at MAP, and especially by neglecting to bring in representatives of the target user communities to conduct such tasks. This paper reports on the use of within-subjects experiments to reduce subject variability and emphasize the examination of specific video search interface features for their effectiveness in interactive retrieval and user satisfaction. A series of experiments is surveyed to illustrate the gradual realization of getting non-experts to utilize non-textual query features through interface adjustments. Notably, the paper explores the use of the search system by government intelligence analysts, concluding that a variety of search methods are useful for news video retrieval and lead to improved satisfaction. This community, dominated by text search system expertise but still new to video and image search, performed better with and favored a system with image and concept query capabilities over an exclusive text-search system. The user study also found that sports topics mean nothing for this user community and tens of relevant shots collected into the answer set are considered enough to satisfy the information need. Lessons learned from these user interactions are reported, with recommendations on both interface improvements for video retrieval systems and enhancing the ecological validity of video retrieval interface evaluations.

[1]  Ben Shneiderman,et al.  Clarifying Search: A User-Interface Framework for Text Searches , 1997, D Lib Mag..

[2]  Laura A. Dabbish,et al.  Labeling images with a computer game , 2004, AAAI Spring Symposium: Knowledge Collection from Volunteer Contributors.

[3]  Alexander G. Hauptmann,et al.  Successful approaches in the TREC video retrieval evaluations , 2004, MULTIMEDIA '04.

[4]  Marcel Worring,et al.  Assessing user behaviour in news video retrieval : Recent advances in image and video retrieval , 2005 .

[5]  Michael G. Christel,et al.  Finding the right shots: assessing usability and performance of a digital video library interface , 2004, MULTIMEDIA '04.

[6]  Marcel Worring,et al.  Learned Lexicon-Driven Interactive Video Retrieval , 2006, CIVR.

[7]  Gary Marchionini,et al.  The relative effectiveness of concept-based versus content-based video retrieval , 2004, MULTIMEDIA '04.

[8]  Rong Yan,et al.  Efficient Margin-Based Rank Learning Algorithms for Information Retrieval , 2006, CIVR.

[9]  Michael G. Christel,et al.  Mining Novice User Activity with TRECVID Interactive Retrieval Tasks , 2006, CIVR.

[10]  Mor Naaman,et al.  HT06, tagging paper, taxonomy, Flickr, academic article, to read , 2006, HYPERTEXT '06.

[11]  Ben Shneiderman,et al.  Strategies for evaluating information visualization tools: multi-dimensional in-depth long-term case studies , 2006, BELIV '06.

[12]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Michael G. Christel,et al.  Addressing the challenge of visual information access from digital image and video libraries , 2005, Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL '05).

[14]  John Adcock,et al.  Interactive search in large video collections , 2005, CHI EA '05.

[15]  John R. Smith,et al.  Large-scale concept ontology for multimedia , 2006, IEEE MultiMedia.

[16]  Marcel Worring,et al.  A Learned Lexicon-Driven Paradigm for Interactive Video Retrieval , 2007, IEEE Transactions on Multimedia.