Classifier ensembles for image identification using multi-objective Pareto features

In this paper we propose classifier ensembles that use multiple Pareto image features for invariant image identification. Different from traditional ensembles that focus on enhancing diversity by generating diverse base classifiers, the proposed method takes advantage of the diversity inherent in the Pareto features extracted using a multi-objective evolutionary Trace transform algorithm. Two variants of the proposed approach have been implemented, one using multilayer perceptron neural networks as base classifiers and the other k-Nearest Neighbor. Empirical results on a large number of images from the Fish-94 and COIL-20 datasets show that on average, the proposed ensembles using multiple Pareto features perform much better than both, the traditional classifier ensembles of single Pareto features with data randomization, and the well-known Random Forest ensemble. The better classification performance of the proposed ensemble is further supported by diversity analysis using a number of measures, indicating that the proposed ensemble consistently produces a higher degree of diversity than traditional ones. Our experimental results demonstrate that the proposed classifier ensembles are robust to various geometric transformations in images such as rotation, scale and translation, and to additive noise.