Food image classification and image retrieval based on visual features and machine learning

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.