Image Retrieval Using Fused Deep Convolutional Features
Hailong Liu, Baoan Li, Xueqiang Lv, Yue Huang · Procedia Computer Science · 2017
This paper proposes an image retrieval using fused deep convolutional features to solve the semantic gap between low-level features and high-level semantic features of traditional contend-based image retrieval method. Firstly, the improved network architecture LeNet-L is obtained by improving convolutional neural network LeNet-5. Then, fusing two different deep convolutional features which are extracted by LeNet-5 and AlexNet. Finally, after the fusion, the similar image is obtained through comparing the similarity between the image being retrieved and the image in database by distance function. In Corel dataset, this method is compared with the single convolutional neural network extracted features for image retrieval method, it has a higher precision and recall. The results show that this method has a better retrieval accuracy.