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Scale invariance is a desirable property for many vision tasks such as image segmentation and classification. One way to achieve such invariance is to collect images containing objects of all scales and then train a classifie r. In practice, however, only a finite number of images at a finite number of scales can be collected, and this poses the problem of scale sampling. In this paper, we focus on...
In image classification, multi-scale information is usually combined by concatenating features or selecting scales. Their main drawbacks are that concatenation increases the feature dimensionality by the number of scales and scale selection typically loses the information from other scales. We propose to solve this problem by the dissimilarity representation as it enables to combine various sources...
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