Image ordinal classification with deep multi-view learning

Image ordinal classification has drawn substantial attention from the research community due to the ordering relation between image categories. Recent advancements towards image ordinal classification lie in applying deep neural networks [convolutional neural network (CNN)]. Nevertheless, the lack of ordinal training data prevents deep models from generalising to testing data. In this work, two multi-view learning approaches are proposed to tackle the insufficient data issue. On one hand, a multi-view ordinal classification with multi-view max pooling (MVMP) approach is proposed, in which each image is randomly blocked with some grids thus creating multiple views of the original data. All views are then used to train multi-view CNN for classification. On the other hand, in order to account for the ordinal relation, the authors propose a double-task learning on MVMP for classification and average pooling for regression. The task of regression benefits that of classification, mainly focusing on improving classification's recognition accuracy. The two proposed approaches are validated on Adience dataset, and show very compelling results. The code and models will be available online.