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In studies of hepatic phagocyte depletion in mice, we found that myeloid precursors can differentiate into liver macrophages and dendritic cells, which each localize to distinct tissue compartments. During replenishment, macrophages acquire the ability to respond appropriately to hepatic injury and to remove bacteria from the blood stream.

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In this paper we presented a violence detector built on the concept of visual codebooks using linear support vector machines. It differs from the existing works of violence detection in what concern the data representation, as none has considered local spatio-temporal features with bags of visual words. An evaluation of the importance of local spatio-temporal features for characterizing the multimedia content is conducted through the cross-validation method. The results obtained confirm that motion patterns are crucial to distinguish violence from regular activities in comparison with visual descriptors that rely solely on the space domain.

In image classification, the most powerful statistical learning approaches are based on the Bag-of-Words paradigm. In this article, we propose an extension of this formalism. Considering the Bag-ofFeatures, dictionary coding and pooling steps, we propose to focus on the pooling step. Instead of using the classical sum or max pooling strategies, we introduced a density function-based pooling strategy. This flexible formalism allows us to better represent the links between dictionary codewords and local descriptors in the resulting image signature. We evaluate our approach in two very challenging tasks of video and image classification, involving very high level semantic categories with large and nuanced visual diversity.

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