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MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
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ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
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ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
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ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
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In this work, we propose BossaNova, a novel representation for content-based concept detection in images and videos, which enriches the Bag-of-Words model. Relying on the quantization of highly discriminant local descriptors by a codebook, and the aggregation of those quantized descriptors into a single pooled feature vector, the Bag-of-Words model has emerged as the most promising approach for concept detection on visual documents. BossaNova enhances that representation by keeping a histogram of distances between the descriptors found in the image and those in the codebook, preserving thus important information about the distribution of the local descriptors around each codeword. Contrarily to other approaches found in the literature, the non-parametric histogram representation is compact and simple to compute. BossaNova compares well with the state-of-the-art in several standard datasets: MIRFLICKR, ImageCLEF 2011, PASCAL VOC 2007 and 15-Scenes, even without using complex combinations of different local descriptors. It also complements well the cutting-edge Fisher Vector descriptors, showing even better results when employed in combination with them. BossaNova also shows good results in the challenging real-world application of pornography detection.