Abstract

Chinese herbal medicine image recognition and retrieval have great potential of practical applications. Several previous studies have focused on the recognition with hand-crafted image features, but there are two limitations in them. Firstly, most of these hand-crafted features are low-level image representation, which is easily affected by noise and background. Secondly, the medicine images are very clean without any backgrounds, which makes it difficult to use in practical applications. Therefore, designing high-level image representation for recognition and retrieval in real world medicine images is facing a great challenge. Inspired by the recent progress of deep learning in computer vision, we realize that deep learning methods may provide robust medicine image representation. In this paper, we propose to use the Convolutional Neural Network (CNN) for Chinese herbal medicine image recognition and retrieval. For the recognition problem, we use the softmax loss to optimize the recognition network; then for the retrieval problem, we fine-tune the recognition network by adding a triplet loss to search for the most similar medicine images. To evaluate our method, we construct a public database of herbal medicine images with cluttered backgrounds, which has in total 5523 images with 95 popular Chinese medicine categories. Experimental results show that our method can achieve the average recognition precision of 71% and the average retrieval precision of 53% over all the 95 medicine categories, which are quite promising given the fact that the real world images have multiple pieces of occluded herbal and cluttered backgrounds. Besides, our proposed method achieves the state-of-the-art performance by improving previous studies with a large margin.

Highlights

  • Chinese herbal medicine image recognition and retrieval have great potential of applications

  • We propose to use the Convolutional Neural Network (CNN) for Chinese herbal medicine image recognition and retrieval

  • We propose to use the Convolutional Neural Network (CNN) for herbal medicine image recognition and retrieval

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Summary

Introduction

Chinese herbal medicine image recognition and retrieval have great potential of applications. Given a herbal medicine image, herbal medicine image recognition aims to recognize its medicine category, while herbal medicine image retrieval aims to find its similar images. In the traditional way, recognizing medicine category based on herbal medicine images requires. Herbal Medicine Image Recognition and Retrieval professional knowledge, and the hundreds of medicine categories make it difficult for beginners. When collecting some medicine images, retrieving herbal medicine images with the text-only way is not reliable because sometimes the names of medicine are not even known. We come up with a question: why not make life easier by recognizing and retrieving herbal medicine images automatically? Recognizing and retrieving medicine images has become an urgent need.

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