Discovering Efficiency in Coarse-To-Fine Texture Classification

We introduce a model for joint texture classification and segmentation that learns not only how to classify accurately, but when to classify efficiently. This model, combined with a complementary efficient feature representation that we describe, allows us to move beyond naive slidingwindow classification strategies into sub-linear coarse-tofine classification of an entire image. Recognition is formulated as a scale-space traversal through the image in which we can “stop short” at coarse scales, dramatically increasing both the speed and the accuracy of classification. Unlike other models, ours is constructed such that the classification produced when stopping-short is exact (that is, equivalent to the classification produced when not stopping-short), because coarse-to-fine efficiency is directly incorporated into the model. Classification is demonstrated on partiallyand fully-annotated datasets of satellite and medical imagery.