Abstract
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.
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Strong, G., Gong, M. (2008). Browsing a Large Collection of Community Photos Based on Similarity on GPU. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89646-3_38
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DOI: https://doi.org/10.1007/978-3-540-89646-3_38
Publisher Name: Springer, Berlin, Heidelberg
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