Mid-level-Representation Based Lexicon for Vehicle Make and Model Recognition

In this paper, we present a novel framework for representation of images as a combination of multiple mid-level feature descriptor representation based group of visual words. The mid-level feature representation is computed on discriminative patches of the image to build a lexicon, the visual words of which are used to represent the shape within that image. The proposed image representation method has been applied to the application of vehicles make and model recognition. Each make, model class is represented as an over complete sub-lexicon of mid-level feature representation. The classification of vehicles is performed by comparing the visual words of probe image with the learned lexicon of training data using Euclidean distance. The proposed framework offers the advantage of accurate recognition in the presence of significant background clutter. The experiments have shown that the proposed representation successfully captures the fine-grained inter and intra-class discrimination to recognize the model and make of the vehicle without any strict requirement of precise region of interest segmentation. Another important contribution of the paper is a comprehensive dataset of cars depicting images collected in the wild.