Spatial and multi-resolution context in visual indexing
Recent trends in visual indexing make appear a large family of methods which use a local image representation via descriptors associated to the interest points, see chapter 2. Such approaches mostly ”forget” any structure in the image considering unordered sets of descriptors or their histograms as image model. Hence, more advanced approaches try to overcome this drawback by adding spatial arrangements to the interest points. In this chapter we will present two trends in incorporation of spatial context into visual description, such as considering spatial context in the process of matching of signatures on one hand and design of structural descriptors which are then used in a global Bag-of-Visual-Words (BoVW) approach on the other hand. As images and video are mainly available in a compressed form, we shortly review global descriptors extracted from compressed stream and hence less sensible to compression artifacts. Furthermore, on the basis of scalable, multiresolution/ multi-scale visual content representation in modern compression standards, we study how this multi-resolution context can be efficiently incorporated into a BoVW approach.
KeywordsVisual Word Interest Point Spatial Context Mean Average Precision Spatial Pyramid Match
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