IMAGE ASSESSMENT USING DEEP CONVOLUTIONAL NEURAL NETWORKS

摘要

Deep convolutional neural networks receive local and global representations of images as inputs and learn the best representation for a particular feature through multiple convolutional and fully connected layers. A double-column neural network structure receives each of the local and global representations as two heterogeneous parallel inputs to the two columns. After some layers of transformations, the two columns are merged to form the final classifier. Additionally, features may be learned in one of the fully connected layers. The features of the images may be leveraged to boost classification accuracy of other features by learning a regularized double-column neural network.

主权项

1. A non-transitory computer storage medium comprising computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform operations comprising:
implementing a deep convolutional neural network that is trained to learn and classify image features for a set of images; receiving an image from the set of images; extracting a local image representation of the image as one or more fine-grained inputs to the deep convolutional neural network; calculating a probability of each input being assigned to a class for a particular feature; averaging results associated with each input associated with the image; and selecting the class with the highest probability.