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As datasets grow increasingly large in content based image and video retrieval, computational efficiency of concept classification is important. This paper reviews techniques to accelerate concept classification, where we show the trade-off between computational efficiency and accuracy. As a basis we use the Bag-of-Words algorithm that in the 2008 benchmarks of TRECVID and PASCAL lead to the best performance scores. We divide the evaluation in three steps: (1) Descriptor Extraction, where we evaluate SIFT, SURF, DAISY, and Semantic Textons. (2) Visual Word Assignment, where we compare a k-means visual vocabulary with a Random Forest and evaluate subsampling, dimension reduction with PCA, and division strategies of the Spatial Pyramid. (3) Classification, where we evaluate the χ 2 , RBF, and Fast Histogram Intersection kernel for the SVM. Apart from the evaluation, we accelerate the calculation of densely sampled SIFT and SURF, accelerate nearest neighbour assignment, and improve accuracy of the Histogram Intersection kernel. We conclude by discussing whether further acceleration of the Bag-of-Words pipeline is possible. Our results lead to a 7-fold speed increase without accuracy loss, and a 70-fold speed increase with 3% accuracy loss. The latter system does classification in real-time, which opens up new applications for automatic concept classification. For example, this system permits 5 standard desktop PCs to automatically tag for 20 classes all images that are currently uploaded to Flickr.

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In data-oriented language processing, an annotated language corpus is used as a stochastic grammar. The most probable analysis of a new sentence is constructed by combining fragments from the corpus in the most probable way. This approach has been successfully used for syntactic analysis, using corpora with syntactic annotations such as the Penn Tree-bank. If a corpus with semantically annotated sentences is used, the same approach can also generate the most probable semantic interpretation of an input sentence. The present paper explains this semantic interpretation method. A data-oriented semantic interpretation algorithm was tested on two semantically annotated corpora: the English ATIS corpus and the Dutch OVIS corpus. Experiments show an increase in semantic accuracy if larger corpus-fragments are taken into consideration.