In this paper we study automatic recognition of cars of four types: Bus, Truck, Van and Small car. For this problem we consider two data driven frameworks: a deep neural network and a support vector machine using SIFT features. The accuracy of the methods is validated with a database of over 6500 images, and the resulting prediction accuracy is over 97 %. This clearly exceeds the accuracies of earlier studies that use manually engineered feature extraction pipelines.

Alignment of semantically meaningful visual patterns, such as object classes, is an important pre-processing step for a number of applications such as object detection and image categorization. Considering the expensive manpower spent on the annotation for supervised alignment methods, unsupervised alignment techniques are more favorable especially for large-scale problems. Fine adjustment can be effectively and efficiently achieved with image congealing methods, but they require moderately good initialization which is largely invalid in practice. Alignment of visual class examples with large view point changes remains as an open problem. Feature-based methods can solve the problem to some degree, but require manual selection of a good seed image and omit the fact that examples of a semantic class can be visually very different (e.g., Harley-Davidsons and Scooters in “motorbikes”). In this work, we overcome the aforementioned drawbacks by defining visual similarity under the generalized assignment problem which is solved by fast approximation and non-linear optimization. From pair-wise image similarities we construct an image graph which is used to step-wise align, “morph”, an image to another by graph traveling. We automatically find a suitable seed by novel centrality measure which identifies “similarity hubs” in the graph. The proposed approach in the unsupervised manner outperforms the state-of-the-art methods with classes from the popular benchmark datasets.