Semantic driven hierarchical learning for energy-efficient image classification

摘要

Machine-learning algorithms have shown outstanding image recognition performance for computer vision applications. While these algorithms are modeled to mimic brain-like cognitive abilities, they lack the remarkable energy-efficient processing capability of the brain. Recent studies in neuroscience reveal that the brain resolves the competition among multiple visual stimuli presented simultaneously with several mechanisms of visual attention that are key to the brain's ability to perform cognition efficiently. One such mechanism known as saliency based selective attention simplifies complex visual tasks into characteristic features and then selectively activates particular areas of the brain based on the feature (or semantic) information in the input. Interestingly, we note that there is a significant similarity among underlying characteristic semantics (like color or texture) of images across multiple objects in real world applications. This presents us with an opportunity to decompose a large classification problem into simpler tasks based on semantic or feature similarity. In this paper, we propose semantic driven hierarchical learning to construct a tree-based classifier inspired by the biological visual attention mechanism for optimizing energy-efficiency of machine-learning classifiers. We exploit the inherent feature similarity across images to identify the input variability and use recursive optimization procedure, to determine data partitioning at each tree node, thereby, learning the feature hierarchy. A set of binary classifiers is organized on top of the learnt hierarchy to minimize the overall test-time complexity. The feature based-learning allows selective activation of only those branches and nodes of the classification tree that are relevant to the input while keeping the remaining nodes idle. The proposed framework has been evaluated on Caltech-256 dataset and achieves ∼3.7x reduction in test complexity for 1.2% accuracy improvement over state-of-the-art one-vs-all tree-based method, and even higher improvements in test-time (of ∼5.5x) when some loss in output accuracy (up to 2.5%) is acceptable.