Qualitative photo collage by quartet analysis and active learning

Abstract

In order to select representative images from a large image set to construct an album, we propose a new album management method in a comic-like layout based on images classification. We mainly focus on three key problems: how to organize the input set of images reasonably; how to classify and select representative images rapidly and accurately according to the user intent; how to display the representative images clearly. For the first problem, we use the model organization idea of Quartet Analysis, analyzing the set of images by the multiple global features, and we realize the effective organization of the images through the construction of the categorization tree (C-tree). For the second problem, in order to express the user intent better, we perform fine classification by using active learning method based on the C-tree. Then we recommend representative images for the user to select. For the third problem, we display representative images in a compact comic-like layout with hierarchical ordering information. To enrich layout styles, we create a template library based on enumeration by comic layout rules and complete the template selection and automatic collage of the images based on the principle of presenting the maximum information content of the images to be displayed. Experiments demonstrate that our method can not only assist the user with album production but also involve users in the whole process of making an album.