A Novel Semantics-Preserving Hashing for Fine-Grained Image Retrieval

With the advent of the era of big data, the storage and retrieval of data have become a research hotspot. Hashing methods that transform high-dimensional data into compact binary codes have received increasing attention. Recently, with the successful application of convolutional neural networks in computer vision, deep hashing methods utilize an end-to-end framework to learn feature representations and hash codes mutually, which achieve better retrieval performance than conventional hashing methods. However, deep hashing methods still face some challenges in image retrieval. Firstly, most existing deep hashing methods preserve similarity between original data space and hash coding space using loss functions with high time complexity, which cannot get a win-win situation in time and accuracy. Secondly, few existing deep hashing methods are designed for fine-grained image retrieval, which is necessary in practice. In this study, we propose a novel semantics-preserving hashing method which solves the above problems. We add a hash layer before the classification layer as a feature switch layer to guide the classification. At the same time, we replace the complicated loss with the simple classification loss, combining with quantization loss and bit balance loss to generate high-quality hash codes. Besides, we incorporate feature extractor designed for fine-grained image classification into our network for better representation learning. The results on three widely-used fine-grained image datasets show that our method is superior to other state-of-the-art image retrieval methods.