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This paper presents a novel Bag of Features (BoF) method for image classification. The BoF approach describes an image as a set of local descriptors using a histogram, where each bin represents the importance of a visual word. This indexing approach has been frequently used for image classification, and we have seen several implementations, but crucial representation choices - such as the weighting...
Recent works on Content Based Image Retrieval rely on Bag of Visual Words to index images. Analogically to the Bag of Words approach used in text retrieval, this model allows describing an image as a bag of elementary local features called visual words. As a result, an image is represented by a vector of weights, where each weight corresponds to the importance of a visual word in the image. The choice...
We present a lattice-based STD method for German broadcast news data and compare it to a previously proposed fuzzy search. Due to the important out-of-vocabulary (OOV) problem in German, we evaluate suitable subword indexing units for lattice retrieval. Hybrid lattice retrieval of words and subwords is investigated because of the robust nature of words as an indexing unit. We show that by using efficient...
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