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.
翻译:文件分析、医学图像分析等都需要使用视觉特征进行快速和可缩放的内容图像检索。 进化神经网络(CNN)的激活是其在这方面取得杰出性能的特征。 使用输出层软负函数的深演化也是视觉特征之一。 然而,几乎所有图像检索系统都将其视觉特征索引保存在主要记忆中,以便高响应性,限制其适用于大数据应用。 在本文中,我们提出了一个快速计算方法,即与L2标准类似,在 Elasticresear 上提前进行索引。我们评估了我们对图像网数据集和VGG-16预培训模型的处理方法。 评估结果显示了我们拟议方法的有效性和效率。