Sequential three-way decisions in multi-category image recognition with deep features based on distance factor
The paper addresses the issue of insufficient speed of image recognition methods if the number of classes is rather large. We propose the novel algorithm based on sequential three-way decisions and a formal description of granular computing. Each image is associated with principal component scores of the high-dimensional features extracted by deep convolution neural network. Low number of principal components stand for the coarse-grained granules, while fine-grained granules include all components. Initially, first principal components of an observed image and all training instances are matched at the coarsest granularity level. Next, negative decisions are defined by using the multiple comparisons theory and asymptotic distribution of the Kullback-Leibler divergence. Namely, the distance factors (ratios of the minimum distance and all other distances) are evaluated. The set of negative decisions is populated by the instances, for which the distance factors exceed a certain threshold. The images from this set are not examined at the next levels with finer granularity. In the experiments unconstrained face recognition and image categorisation are considered using the state-of-the-art deep learning-based feature extractors. We demonstrate that the proposed approach decreases the running time in 1.5–10 times when compared to conventional classifiers and the known multi-class decision-theoretic rough sets.