Image quality assessment for advertising applications based on neural network

Abstract

Image quality assessment (IQA) is a key technique in computer vision, which is widely applied in image classification, image aesthetic prediction. IQA plays an important role in advertising assessment system, which can recommend higher quality advertising for users. However, traditional algorithms cannot effectively predict advertising quality. In this paper, we propose an advertising assessment system using IQA algorithm based on neural networks. Specifically, we first incorporate both low-level features and high-level sematic features for image representation, where manifold learning algorithm is leveraged for high-level feature learning. Then, we leverage CNN based method for deep representation learning, which will be concatenated into deep feature vector. Finally, we leverage HMM model for learning image quality of advertising based on learned feature vector. Comprehensive experiments show the effectiveness of our proposed method.