Unsupervised multi-view object proposal ranking

Hong Man; Shuanglu Dai; Victor Lawrence; Thomas LaPeruta; Myron Hohil

doi:10.1117/12.2587810

12 April 2021 Unsupervised multi-view object proposal ranking

Hong Man, Shuanglu Dai, Victor Lawrence, Thomas LaPeruta, Myron Hohil

Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 117460K (2021) https://doi.org/10.1117/12.2587810
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

This paper proposes a novel region-based structure measure for object proposal ranking. It is able to efficiently reduce redundant object proposals and highlight dominant objects in an image. The computation of this measure is fully unsupervised, without any image level annotation or any visual semantics labeling. In this work, a new set of heuristic rules are introduced to indicate regions that may contain objects. The distinctiveness of a proposal region is assessed based on its structural uniqueness, structural distributions and deformable shapes. A scoring function is then constructed to combine these multi-view rule-based assessments into a single object score. Furthermore, a rank-recall optimization is proposed to optimize the scoring function for proposal ranking. The final optimized ranking significantly reduces the number of object proposals while maintaining potential object regions. Promising results show that the proposed ranking method simultaneously reduces proposals and highlights dominant objects.

Conference Presentation

Citation Download Citation

Hong Man, Shuanglu Dai, Victor Lawrence, Thomas LaPeruta, and Myron Hohil "Unsupervised multi-view object proposal ranking", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117460K (12 April 2021); https://doi.org/10.1117/12.2587810

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