1 research outputs found
A Fast Content-Based Image Retrieval Method Using Deep Visual Features
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.Comment: accepted in ICDAR-WML: The 2nd International Workshop on Machine
Learning 201