Relative attributes with deep Convolutional Neural Network
Dong-Jin Kim, Donggeun Yoo, Sunghoon Im, Nam Il Kim, Tharatch Sirinukulwattana, In So Kweon · 2015
Our work is based on the idea of relative attributes, aiming to provide more descriptive information to the images. We propose the model that integrates relative-attribute framework with deep Convolutional Neural Networks (CNN) to increase the accuracy of attribute comparison. In addition, we analyzed the role of each network layer in the process. Our model uses features extracted from CNN and is learned by Rank SVM method with these feature vectors. As a result, our model outperforms the original relative attribute model in terms of significant improvement in accuracy.