Attribute-correlated local regions for deep relative attributes learning

Abstract

Relative attributes have a more detailed and accurate description than previous binary ones. We propose to utilize the acquired attribute-correlated local regions of image for learning deep relative attributes. Different from previous works, which usually discover the spatial extent of the corresponding attribute based on the ranking list of all the images in the image set, we first classify the images according to the presence or absence of each provided attribute. Then, we sort the images in the classified image sets using a semisupervised method and learn the most relevant regions corresponding to a specific attribute. The learned local regions in two classified image sets are integrated to obtain the final result. The images and localized regions are then fed into the pretrained convolutional neural network model for feature extraction. Therefore, the concatenation of the high-level global feature and intermediate local feature is adopted to predict the relative attributes. We show that the proposed method produces a competitive performance compared with the state of the art in relative attribute prediction on three public benchmarks.