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Deep learning is increasingly used in the field of computer vision, and animal image retrieval algorithms are an important application scenario. This article will introduce an animal image retrieval algorithm based on deep learning and provide corresponding code implementation.
1. Algorithm Principle
This algorithm uses Convolutional Neural Network (CNN) as a feature extractor to convert images into feature vectors. Then, the retrieval results are obtained by calculating the similarity between feature vectors.
Specifically, we adopt the pre-trained ResNet50 model as the feature extractor. This model has achieved good results when trained on the ImageNet data set and can effectively extract features of images.
For the input image, we input it into the ResNet50 model and obtain a 2048-dimensional feature vector. Then, we save the feature vectors of all images for subsequent similarity calculations.
For the query image, we also input it into the ResNet50 model and obtained a 2048-dimensional feature vector. Then, we calculate the cosine similarity between this feature vector and the feature vectors of all images to obtain a similarity vector. Finally, we use the top K pictures with the highest similarity in the similarity vector as the retrieval results.
2. Code implementation
The following is based on Python and TensorFlo