Supervised Deep Feature Embedding with Hand Crafted Feature

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Shichao KanYanhong Wang

Abstract

Image representation methods based on deep convolutional neural networks (CNNs) have achieved state-of-the-art performance in various computer vision tasks, such as image retrieval and person re-identification. We recognize that more discriminative feature embeddings can be learned with supervised deep metric learning and hand crafted features for image retrieval and similar applications. In this paper, we propose a new supervised deep feature embedding with a hand crafted feature model. To fuse hand crafted feature information into CNNs and realize feature embeddings, a general fusion unit is proposed (called Fusion-Net). We also define a network loss function with image label information to realize supervised deep metric learning. Our extensive experimental results on the Stanford Online Products' data set and the In-shop Clothes Retrieval data set demonstrate that our proposed methods outperform existing state-of-the-art methods of image retrieval with a large margin. Moreover, we also explore the applications of the proposed methods in person re-identification and vehicle re-identification; both experimental results demonstrate the effectiveness and efficiency of the proposed methods.

Citations

Sep 27, 2020·Computers in Biology and Medicine·Muhammad Khawar SanaMuhammad Haisum Maqsood
Dec 11, 2020·Computational Intelligence and Neuroscience·Guang-Hai Liu, Zhao Wei

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