POI summarization by aesthetics evaluation from crowd source social media

Place-of-Interest (POI) summarization by aesthetics evaluation can recommend a set of POI images to the user and it is significant in image retrieval. In this paper, we propose a system that summarizes a collection of POI images regarding both aesthetics and diversity of the distribution of cameras. First, we generate visual albums by a coarse-to-fine POI clustering approach and then generate 3D models for each album by the collected images from social media. Second, based on the 3D to 2D projection relationship, we select candidate photos in terms of the proposed crowd source saliency model. Third, in order to improve the performance of aesthetic measurement model, we propose a crowd-sourced saliency detection approach by exploring the distribution of salient regions in the 3D model. Then, we measure the composition aesthetics of each image and we explore crowd source salient feature to yield saliency map, based on which, we propose an adaptive image adoption approach. Finally, we combine the diversity and the aesthetics to recommend aesthetic pictures. Experimental results show that the proposed POI summarization approach can return images with diverse camera distributions and aesthetics.