Discovering and Aligning Discriminative Mid-level Features for Image Classification

This paper proposes a new algorithm for image recognition, which consists of (i) modeling categories as a set of distinctive parts that are discovered automatically, (ii) aligning them across images while learning their visual model, and, finally (iii) encode images as sets of part descriptors. The so-obtained parts are free of any appearance constraint and are optimized to allow the distinction between the categories to be recognized. The algorithm starts by extracting a set of random regions from the images of different classes, and, using a soft assign-like matching algorithm, simultaneously learns the model of each part and assigns image regions to the model´s parts. Once the model of the category is trained, it can be used to classify new images by first finding image´s regions similar to learned parts and encoding them by the fisher-on-parts encoding, which is another contribution of this paper. The proposed framework is experimentally validated on two publicly available datasets, on which state-of-the-art performance is obtained.