Diverse M-Best Solutions by Dynamic Programming

TL;DR

This paper introduces a dynamic programming approach to efficiently compute the top M solutions in tree-shaped graphical models, enhancing candidate diversity for computer vision tasks.

Contribution

It presents a novel multi-layer dynamic programming method for obtaining M-best and diverse solutions in tree-structured models, improving over existing techniques.

Findings

The method is optimal for M-best solutions in tree models.

It can approximate diverse solutions effectively.

Applications include object detection, panorama stitching, and centerline extraction.

Abstract

Many computer vision pipelines involve dynamic programming primitives such as finding a shortest path or the minimum energy solution in a tree-shaped probabilistic graphical model. In such cases, extracting not merely the best, but the set of M-best solutions is useful to generate a rich collection of candidate proposals that can be used in downstream processing. In this work, we show how M-best solutions of tree-shaped graphical models can be obtained by dynamic programming on a special graph with M layers. The proposed multi-layer concept is optimal for searching M-best solutions, and so flexible that it can also approximate M-best diverse solutions. We illustrate the usefulness with applications to object detection, panorama stitching and centerline extraction. Note: We have observed that an assumption in section 4 of our paper is not always fulfilled, see the attached corrigendum for details.