A novel approach is proposed in this paper to facilitate browsing a large collection of community photos based on visual similarities. Using extracted feature vectors, the approach maps photos onto a 2D rectangular area such that the ones with similar features are close to each other. When a user browses the collection, a subset of photos is automatically selected to compose a photo collage. Once having identified photos of interest the user can find more photos with similar features through panning and zooming operations, which dynamically update the photo collage. To quickly organize a large number of photos, the 2D mapping process is performed on the GPU, which yields 15~19 times speedup over the CPU implementation. © 2008 Springer Berlin Heidelberg.
CITATION STYLE
Strong, G., & Gong, M. (2008). Browsing a large collection of community photos based on similarity on GPU. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5359 LNCS, pp. 390–399). https://doi.org/10.1007/978-3-540-89646-3_38
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