  One of the key challenges in machine learning is to design a computationally
efficient multi-class classifier while maintaining the output accuracy and
performance. In this paper, we present a tree-based classifier: Attention Tree
(ATree) for large-scale image classification that uses recursive Adaboost
training to construct a visual attention hierarchy. The proposed attention
model is inspired from the biological 'selective tuning mechanism for cortical
visual processing'. We exploit the inherent feature similarity across images in
datasets to identify the input variability and use recursive optimization
procedure, to determine data partitioning at each node, thereby, learning the
attention hierarchy. A set of binary classifiers is organized on top of the
learnt hierarchy to minimize the overall test-time complexity. The attention
model maximizes the margins for the binary classifiers for optimal decision
boundary modelling, leading to better performance at minimal complexity. The
proposed framework has been evaluated on both Caltech-256 and SUN datasets and
achieves accuracy improvement over state-of-the-art tree-based methods at
significantly lower computational cost.
