A Multi-Level Machine Learning System for Attention-Based Object Recognition

This thesis develops a trainable object-recognition algorithm. This algorithm represents objects using their salient features. The algorithm applies an attention mechanism to speed up feature detection. A trainable component-based object recognition system which implements the developed algorithm has been created. This system has two layers. The first layer contains several individual feature classifiers. They detect salient features which compose higher level objects from input images. The second layer judges if those detected features form a valid object. An object is represented by a feature map which stores the geometrical and hierarchical relations among features and higher level objects. It is the input to the second layer. The attention mechanism is applied to improve feature detection speed. This mechanism will lead the system to areas with a higher likelihood of containing features when a few features are detected. Therefore the feature detection will be sped up. Two major experiments are conducted. These experiments applied the de- veloped system to discriminate faces from non-faces and to discriminate people from backgrounds in thermal images. The results of these experiments show the success of the implemented system. The attention mechanism displays a positive effect on feature detection. It can save feature detection time, especially in terms of classifier calls.