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Computer vision tasks are traditionally defined and evaluated using semanticcategories. However, it is known to the field that semantic classes do notnecessarily correspond to a unique visual class (e.g. inside and outside of acar). Furthermore, many of the feasible learning techniques at hand cannotmodel a visual class which appears consistent to the human eye. These problemshave motivated the use of 1) Unsupervised or supervised clustering as apreprocessing step to identify the visual subclasses to be used in amixture-of-experts learning regime. 2) Felzenszwalb et al. part model and otherworks model mixture assignment with latent variables which is optimized duringlearning 3) Highly non-linear classifiers which are inherently capable ofmodelling multi-modal input space but are inefficient at the test time. In thiswork, we promote an incremental view over the recognition of semantic classeswith varied appearances. We propose an optimization technique whichincrementally finds maximal visual subclasses in a regularized riskminimization framework. Our proposed approach unifies the clustering andclassification steps in a single algorithm. The importance of this approach isits compliance with the classification via the fact that it does not need toknow about the number of clusters, the representation and similarity measuresused in pre-processing clustering methods a priori. Following this approach weshow both qualitatively and quantitatively significant results. We show thatthe visual subclasses demonstrate a long tail distribution. Finally, we showthat state of the art object detection methods (e.g. DPM) are unable to use thetails of this distribution comprising 50\% of the training samples. In fact weshow that DPM performance slightly increases on average by the removal of thishalf of the data.

Self-tuned Visual Subclass Learning with Shared Samples An Incremental Approach