.png){kind=icon}
.png){kind=icon}
Return
ListNet-based object proposals ranking
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2017.06.008.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In object detection, object proposal methods have been widely used to generate candidate regions which may contain objects. Object proposal based on superpixel merging is one kind of object proposal methods, and the merging strategies of superpixels have been extensively explored. However, the ranking of generated candidate proposals still remains to be further studied. In this paper, we formulate the ranking of object proposals as a learning to rank problem, and propose a novel object proposals ranking method based on ListNet. In the proposed method, Selective Search, which is one of the state-of-the-art object proposal methods based on superpixel merging, is adopted to generate the candidate proposals. During the superpixel merging process, five discriminative objectness features are extracted from superpixel sets and the corresponding bounding boxes. Then, to weight each feature, a linear neural network is learned based on ListNet. Consequently, objectness scores can be computed for final candidate proposals ranking. Extensive experiments demonstrate the effectiveness and robustness of the proposed method. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Object proposal
Object detection
Superpixel merging
Objectness feature
ListNet
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W