Deep convolutional neural networks have been success fully applied to image classification tasks. When these same networks have been applied to image retrieval, the assump tion has been made that the last layers would give the best peiformance, as they do in classification. We show that for instance-level image retrieval, lower layers often per form better than the last layers in convolutional neural net works. We present an approach for extracting convolu tional features from dif f erent layers of the networks, and adopt VLAD encoding to encode features into a single vec tor for each image. We investigate the effect of dif f erent layers and scales of input images on the peiformance of convolutional features using the recent deep networks Ox fordNet and GoogLeNet. Experiments demonstrate that in termediate layers or higher layers with finer scales pro duce better results for image retrieval, compared to the last layer. When using compressed 12S-D VLAD descriptors, our method obtains state-of-the-art results and outpeiforms other VLAD and CNN based approaches on two out of three test datasets. Our work provides guidance for transferring deep networks trained on image classification to image re trieval tasks.
Ng et al. REF have conducted thorough experiments to investigate the performance of features from different layers of CNN and different scales of input test images in instance-level image retrieval.
Exploiting local features from deep networks for image retrieval
2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Computer Science