G2MF-WA: Geometric Multi-Model Fitting with Weakly Annotated Data

• URL: http://arxiv.org/abs/2001.06965v1
• Date: Mon, 20 Jan 2020 04:22:01 GMT
• Title: G2MF-WA: Geometric Multi-Model Fitting with Weakly Annotated Data
• Authors: Chao Zhang, Xuequan Lu, Katsuya Hotta, and Xi Yang
• Abstract summary: In weak annotating, most of the manual annotations are supposed to be correct yet inevitably mixed with incorrect ones. We propose a novel method to make full use of the WA data to boost the multi-model fitting performance.
• Score: 15.499276649167975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we attempt to address the problem of geometric multi-model fitting with resorting to a few weakly annotated (WA) data points, which has been sparsely studied so far. In weak annotating, most of the manual annotations are supposed to be correct yet inevitably mixed with incorrect ones. The WA data can be naturally obtained in an interactive way for specific tasks, for example, in the case of homography estimation, one can easily annotate points on the same plane/object with a single label by observing the image. Motivated by this, we propose a novel method to make full use of the WA data to boost the multi-model fitting performance. Specifically, a graph for model proposal sampling is first constructed using the WA data, given the prior that the WA data annotated with the same weak label has a high probability of being assigned to the same model. By incorporating this prior knowledge into the calculation of edge probabilities, vertices (i.e., data points) lie on/near the latent model are likely to connect together and further form a subset/cluster for effective proposals generation. With the proposals generated, the $\alpha$-expansion is adopted for labeling, and our method in return updates the proposals. This works in an iterative way. Extensive experiments validate our method and show that the proposed method produces noticeably better results than state-of-the-art techniques in most cases.

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