Research on image retrieval and classification in the food field has become one of the more and more concerned research topics in the field of multimedia analysis and applications. In recent years, with the rapid development of the Internet industry and multimedia technology, image classification and retrieval technology has become a research hotspot at home and abroad. Traditional keyword-based image retrieval and image classification have been unable to meet people’s daily needs; so, image recognition methods based on image content came into being. The most representative of image feature description methods are mainly two aspects: image visual features and image abstract semantics extracted based on machine learning algorithms. These two algorithms have their own key points in describing images, which are difficult to achieve the desired results in image classification and image retrieval. Based on this, this paper proposes research on food image classification and image retrieval methods based on visual features and machine learning. This paper proposes a food image retrieval and classification method based on Faster R-CNN network. This paper selects food image sets from the visual gene database to fine-tune the Faster R-CNN network to ensure the accuracy of Faster R-CNN food area detection, and experimented on the Dish-233 food dataset, which is a subset of the dish dataset, including 233 dishes and 49,168 images. The experimental results in this paper show that the performance of this method is better than other methods in terms of image classification performance. Compared with CNN-GF, the performance is improved by 5%. In terms of image retrieval, this method also shows its superiority This proves that compared with other methods, the proposed method has more discriminative visual features, and its performance has been improved in food image retrieval and classification tasks.

中文翻译：

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基于视觉特征和机器学习的食品图像分类与图像检索

食品领域的图像检索与分类研究已成为多媒体分析与应用领域越来越受关注的研究课题之一。近年来，随着互联网产业和多媒体技术的飞速发展，图像分类检索技术成为国内外研究热点。传统的基于关键词的图像检索和图像分类已经无法满足人们的日常需求；于是，基于图像内容的图像识别方法应运而生。最具代表性的图像特征描述方法主要有两个方面：基于机器学习算法提取的图像视觉特征和图像抽象语义。这两种算法在描述图像方面都有各自的关键点，在图像分类和图像检索中难以达到预期的结果。基于此，本文提出了基于视觉特征和机器学习的食品图像分类和图像检索方法研究。本文提出了一种基于Faster R-CNN网络的食物图像检索和分类方法。本文从视觉基因数据库中选择食物图像集对 Faster R-CNN 网络进行微调，以保证 Faster R-CNN 食物区域检测的准确性，并在 Dish-233 食物数据集上进行了实验，该数据集是菜肴数据集，包括 233 个菜肴和 49,168 张图像。本文的实验结果表明，该方法在图像分类性能方面优于其他方法。与CNN-GF相比，性能提升了5%。