Fast and scalable Content-Based Image Retrieval using visual features is required for document analysis, Medical image analysis, etc. in the present age. Convolutional Neural Network (CNN) activations as features achieved their outstanding performance in this area. Deep Convolutional representations using the softmax function in the output layer are also ones among visual features. However, almost all the image retrieval systems hold their index of visual features on main memory in order to high responsiveness, limiting their applicability for big data applications. In this paper, we propose a fast calculation method of cosine similarity with L2 norm indexed in advance on Elasticsearch. We evaluate our approach with ImageNet Dataset and VGG-16 pre-trained model. The evaluation results show the effectiveness and efficiency of our proposed method.
@article{arxiv.1908.01505,
title = {A Fast Content-Based Image Retrieval Method Using Deep Visual Features},
author = {Hiroki Tanioka},
journal= {arXiv preprint arXiv:1908.01505},
year = {2023}
}
Comments
accepted in ICDAR-WML: The 2nd International Workshop on Machine Learning 2019