Estimation of Membership Functions for Pattern Recognition and Computer Vision

The paper is intended to provide an overview of advanced robotic technologies within the context of Postal Automation services. The main functional requirements of the application are briefly referred, as well as the state of the art and new emerging solutions. Image Processing and Pattern Recognition have always played a fundamental role in Address Interpretation and Mail sorting and the new challenging objective is now off-line handwritten cursive recognition, in order to be able to handle all kind of addresses in a uniform way. On the other hand, advanced electromechanical and robotic solutions are extremely important to solve the problems of mail storage, transportation and distribution, as well as for material handling and logistics. Finally a short description of new services of Postal Automation is referred, by considering new emerging services of hybrid mail and paper to electronic conversion.

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Binary Image Classification: A Genetic Programming Approach to the Problem of Limited Training Instances

Evolutionary Computation ◽

10.1162/evco_a_00146 ◽

2016 ◽

Vol 24 (1) ◽

pp. 143-182 ◽

Cited By ~ 10

Author(s):

Harith Al-Sahaf ◽

Mengjie Zhang ◽

Mark Johnston

Keyword(s):

Computer Vision ◽

Pattern Recognition ◽

Genetic Programming ◽

Visual System ◽

Image Classification ◽

Human Visual System ◽

Binary Classification ◽

Programming Approach ◽

Data Sets ◽

New Class

In the computer vision and pattern recognition fields, image classification represents an important yet difficult task. It is a challenge to build effective computer models to replicate the remarkable ability of the human visual system, which relies on only one or a few instances to learn a completely new class or an object of a class. Recently we proposed two genetic programming (GP) methods, one-shot GP and compound-GP, that aim to evolve a program for the task of binary classification in images. The two methods are designed to use only one or a few instances per class to evolve the model. In this study, we investigate these two methods in terms of performance, robustness, and complexity of the evolved programs. We use ten data sets that vary in difficulty to evaluate these two methods. We also compare them with two other GP and six non-GP methods. The results show that one-shot GP and compound-GP outperform or achieve results comparable to competitor methods. Moreover, the features extracted by these two methods improve the performance of other classifiers with handcrafted features and those extracted by a recently developed GP-based method in most cases.

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