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Supervised Deep Feature Embedding With Handcrafted Feature
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DOI：10.1109/TIP.2019.2901407.png)
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Abstract
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Image representation methods based on deep convolutional neural networks (CNNs) have achieved the state-of-theart 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 handcrafted features for image retrieval and similar applications. In this paper, we propose a new supervised deep feature embedding with a handcrafted feature model. To fuse handcrafted feature information into CNNs and realize feature embeddings, a general fusion unit is proposed (called Fusion-Net). We also define a network lass 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 the existing state-of-the-art methods of image retrieval by a large margin. Moreover, we also explore the applications of the proposed methods in person re-identification and vehicle re-identification; the experimental results demonstrate both the effectiveness and efficiency of the proposed methods.
Keywords:
Deep feature embedding
handcrafted feature
image representation
deep metric learning
image retrieval
person re-identification
vehicle re-identification
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