G2MF-WA: Geometric multi-model fitting with weakly annotated data

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 multimodel 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 α-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-theart techniques in most cases.

Outlier Detection by Energy Minimization in Quantized Residual Preference Space for Geometric Model Fitting

“…The energy-based fitting methods formulate the geometric multi-model fitting as an optimal labeling problem, with a global energy function balancing the geometric errors and the regularity of the inlier clusters, the optimal labeling problem is solved by means of the α-expansion algorithm [31]. Similar graph-based methods [32,33] are proposed to solve this problem. And also hypergraph [34] has been introduced to represent the relation between hypotheses and data points for multi-model fitting [35,36].…”

Outlier Detection by Energy Minimization in Quantized Residual Preference Space for Geometric Model Fitting

“…The energy-based fitting methods formulate the geometric multimodel fitting as an optimal labeling problem, with a global energy function balancing the geometric errors and the regularity of the inlier clusters; the optimal labeling problem is solved by means of the α expansion algorithm [29]. Similar graph-based methods [30,31] have been proposed to solve this problem. Also, the hypergraph [32] has been introduced to represent the relation between hypotheses and data points for multimodel fitting [33,34].…”

PMSSC: Parallelizable multi-subset based self-expressive model for subspace clustering

“…The task of subspace clustering [1,2], which is the segmentation of a set of data points into those lying on certain subspaces, has been studied in many practical applications such as face clustering [3], image segmentation [4], motion segmentation [5], scene segmentation [6], and homography detection [7]. Recently, self-expressive models [8,9] have been explored, which embrace the self-expressive property of subspaces to compute an affinity matrix.…”