Learning Entropy Signature for Image Representation and Classification
Learning Entropy (LE) has recently been extended to image analysis through Spatial Learning Entropy Maps (SLEMs), which are two-dimensional LE distributions that highlight unusually high learning activity across an image. Unlike conventional image descriptors, SLEMs are generated by incremental, sample-wise learning of a pretrained feedforward MLP network, where local pixel neighborhoods are presented sequentially in a fixed spatial order to predict the corresponding central pixels. Consequently, the learning activity at each image location depends not only on its local structure but also on the knowledge acquired from previously processed locations. This paper introduces Learning Entropy...
| Authors | Jan Glaser, Ivo Bukovsky, Noriyasu Homma et al. |
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| Primary category | cs.CV |
| Published | 21 June 2026 |
| Last updated at source | 21 June 2026 |
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