Bag of Surrogate Parts Feature for Visual Recognition

Convolutional neural networks (CNNs) have attracted significant attention in visual recognition. Several recent studies have shown that, in addition to the fully connected layers, the features derived from the convolutional layers of CNNs can also achieve promising performance in image classification tasks. In this paper, we propose a new feature from the convolutional layers, called Bag of Surrogate Parts (BoSP), and its spatial variant, Spatial-BoSP (S-BoSP). The main idea is, we assume the feature maps in the convolutional layers as surrogate parts, and densely sample and assign image regions to these surrogate parts by observing the activation values. Together with BoSP/S-BoSP, we further propose another two schemes to enhance the performance: scale pooling and global-part prediction. Scale pooling aims to handle the objects with different scales and deformations, and global-part prediction combines the predictions of global and part features. By conducting extensive experiments on generic object, fine-grained object and scene datasets, we find the proposed scheme can not only achieve superior performance to the fully connected feature, but also produces competitive or, in some cases, remarkably better performance than the state of the art.