Abstract
Visual concept detection is important to access visual information on the level of objects and scene types. The current state–of–the–art in visual concept detection and annotation tasks is based on the bag–of–words model. Within the bag–of–words model, points are first sampled according to some strategy, then the area around these points are described using color descriptors. These descriptors are then vector–quantized against a codebook of prototypical descriptors, which results in a fixed–length representation of the image. Based on these representations, visual concept models are trained. In this chapter, we discuss the design choices within the bag–of–words model and their implications for concept detection accuracy.
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van de Sande, K.E.A., Gevers, T. (2010). University of Amsterdam at the Visual Concept Detection and Annotation Tasks. In: Müller, H., Clough, P., Deselaers, T., Caputo, B. (eds) ImageCLEF. The Information Retrieval Series, vol 32. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15181-1_18
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DOI: https://doi.org/10.1007/978-3-642-15181-1_18
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