Mr. Michael A. Smith Profile

Michael A. Smith

Multimedia Services Director at Orange SA

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Proceedings Article | 16 January 2006 Paper

Using context and similarity for face and location identification

Marc Davis, Michael Smith, Fred Stentiford, Adetokunbo Bamidele, John Canny, Nathan Good, Simon King, Rajkumar Janakiraman

Proceedings Volume 6061, 60610E (2006) https://doi.org/10.1117/12.650981

KEYWORDS: Image analysis, Facial recognition systems, Cameras, Visualization, Machine vision, Principal component analysis, Analytical research, Computer vision technology, Photography, Visual process modeling

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This paper describes a new approach to the automatic detection of human faces and places depicted in photographs taken on cameraphones. Cameraphones offer a unique opportunity to pursue new approaches to media analysis and management: namely to combine the analysis of automatically gathered contextual metadata with media content analysis to fundamentally improve image content recognition and retrieval. Current approaches to content-based image analysis are not sufficient to enable retrieval of cameraphone photos by high-level semantic concepts, such as who is in the photo or what the photo is actually depicting. In this paper, new methods for determining image similarity are combined with analysis of automatically acquired contextual metadata to substantially improve the performance of face and place recognition algorithms. For faces, we apply Sparse-Factor Analysis (SFA) to both the automatically captured contextual metadata and the results of PCA (Principal Components Analysis) of the photo content to achieve a 60% face recognition accuracy of people depicted in our database of photos, which is 40% better than media analysis alone. For location, grouping visually similar photos using a model of Cognitive Visual Attention (CVA) in conjunction with contextual metadata analysis yields a significant improvement over color histogram and CVA methods alone. We achieve an improvement in location retrieval precision from 30% precision for color histogram and CVA image analysis, to 55% precision using contextual metadata alone, to 67% precision achieved by combining contextual metadata with CVA image analysis. The combination of context and content analysis produces results that can indicate the faces and places depicted in cameraphone photos significantly better than image analysis or context analysis alone. We believe these results indicate the possibilities of a new context-aware paradigm for image analysis.

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