In the paper, we propose a two-stage face-inpainting approach based on conditional generative adversarial networks. In the first stage, a deep-learning model is trained for predicting face landmarks. It also dynamically adjusts the penalty value of the loss function based on the view-degree of a face to improve the ability of predicting high view-degree faces. In the second stage, masked face images and their corresponding face landmarks are concatenated to form the condition of training a conditional Generative Adversarial Network (GAN) for inpainting the masked face. If an input masked image is a nearly-frontal face, an additional procedure for face symmetry processing will be performed before the image is input into the inpainting model. The experimental results show that the proposed training method in the first stage can effectively enhance the robustness of the face-landmark prediction model and reduce the impact of data imbalance, thereby improving the effect of later face inpainting. They also show that the proposed face-inpainting model in the second stage can better maintain the geometric structures and symmetric outlooks of inpainted faces than previous ones.

Nowadays, content-based image retrieval (CBIR) is the mainstay of image retrieval systems. To be more profitable, relevance feedback techniques were incorporated into CBIR such that more precise results can be obtained by taking user's feedbacks into account. However, existing relevance feedback-based CBIR methods usually request a number of iterative feedbacks to produce refined search results, especially in a large-scale image database. This is impractical and inefficient in real applications. In this paper, we propose a novel method, Navigation-Pattern-based Relevance Feedback (NPRF), to achieve the high efficiency and effectiveness of CBIR in coping with the large-scale image data. In terms of efficiency, the iterations of feedback are reduced substantially by using the navigation patterns discovered from the user query log. In terms of effectiveness, our proposed search algorithm NPRFSearch makes use of the discovered navigation patterns and three kinds of query refinement strategies, Query Point Movement (QPM), Query Reweighting (QR), and Query Expansion (QEX), to converge the search space toward the user's intention effectively. By using NPRF method, high quality of image retrieval on RF can be achieved in a small number of feedbacks. The experimental results reveal that NPRF outperforms other existing methods significantly in terms of precision, coverage, and number of feedbacks.