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.