We propose a framework to generate customized summarizations of visual data collections, such as collections of images, materials, 3D shapes, and 3D scenes. We assume that the elements in the visual data collections can be mapped to a set of vectors in a feature space, in which a fitness score for each element can be defined, and we pose the problem of customized summarizations as selecting a subset of these elements. We first describe the design choices a user should be able to specify for modeling customized summarizations and propose a corresponding user interface. We then formulate the problem as a constrained optimization problem with binary variables and propose a practical and fast algorithm based on the alternating direction method of multipliers (ADMM). Our results show that our problem formulation enables a wide variety of customized summarizations, and that our solver is both significantly faster than state-of-the-art commercial integer programming solvers and produces better solutions than fast relaxation-based solvers.
Bibliographical noteKAUST Repository Item: Exported on 2021-07-15
Acknowledgements: This work was partially supported by the National Natural Science Foundation of China (Nos. 62071157, 61620106003, 61772523, 61972459), the KAUST Baseline Funding, the China Postdoctoral Science Foundation (No. 2020M680754), the Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (No. VRLAB2020C03), and the Tencent AI Lab Rhino-Bird Focused Research Program (No. JR202023).
ASJC Scopus subject areas
- Computer Networks and Communications