Global evolution-constructed feature for date maturity evaluation

Abstract

Evolution-Constructed (ECO) Feature as a method to learn image features has achieved very good results on a variety of object recognition and classification applications. When compared with hand-crafted features, ECO-Feature is capable of constructing non-intuitive features that could be overlooked by human experts. Although the ECO features are easy to compute, they are sensitive to small variation of object location and orientation in the images. This paper presents an improved ECO-Feature that addresses these limitations of the original ECO-Feature. The proposed method constructs a global representation of the object and also achieves invariance to small deformations. Two major changes are made in the proposed method to achieve good performance. A non-linear down-sampling technique is employed to reduce the dimensionality of the generated global features and hence improve the training efficiency of ECO-Feature. We apply the global ECO-Feature on a dataset of fruit date to demonstrate the improvement on the original ECO-Feature and the experimental results show the global ECO-Feature’s ability to generate better features for date maturity evaluation.