A number of recent studies have shown that a Deep Con volutional Neural Network (DCNN) pretrained on a large dataset can be adopted as a universal image descriptor, and that doing so leads to impressive peiformance at a range of image classification tasks. Most of these studies, if not all, adopt activations of the fully-connected layer of a DCNN as the image or region representation and it is believed that convolutional layer activations are less discriminative. This paper, however, advocates that if used appropri ately, convolutional layer activations constitute a pow erful image representation. This is achieved by adopt ing a new technique proposed in this paper called cross convolutional-layer pooling. More specifically, it extracts subarrays of feature maps of one convolutional layer as lo cal features, and pools the extracted features with the guid ance of the feature maps of the successive convolutional layer. Compared with existing methods that apply DCNNs in the similar local feature setting, the proposed method avoids the input image style mismatching issue which is usually encountered when applying fully connected layer activations to describe local regions. Also, the proposed method is easier to implement since it is code book free and does not have any tuning parameters. By applying our method to four popular visual classification tasks, it is demonstrated that the proposed method can achieve compa rable or in some cases significantly better performance than existing fully-connected layer based image representations.
For instance, Liu et al. REF took the feature maps as the indicator maps of parts, and aggregated the local features of each surrogate part as the image-level representation.
The treasure beneath convolutional layers: Cross-convolutional-layer pooling for image classification
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science