Summary: A Selective Attention-Based Method for
Visual Pattern Recognition with Application
to Handwritten Digit Recognition
and Face Recognition
Albert Ali Salah, Ethem Alpaydin, and Lale Akarun
Abstract–Parallel pattern recognition requires great computational resources; it is
NP-complete. From an engineering point of view it is desirable to achieve good
performance with limited resources. For this purpose, we develop a serial model
for visual pattern recognition based on the primate selective attention mechanism.
The idea in selective attention is that not all parts of an image give us information.
If we can attend only to the relevant parts, we can recognize the image more
quickly and using less resources. We simulate the primitive, bottom-up attentive
level of the human visual system with a saliency scheme and the more complex,
top-down, temporally sequential associative level with observable Markov models.
In between, there is a neural network that analyses image parts and generates
posterior probabilities as observations to the Markov model. We test our model
first on a handwritten numeral recognition problem and then apply it to a more
complex face recognition problem. Our results indicate the promise of this
approach in complicated vision applications.
Index Terms–Selective attention, Markov models, feature integration, face