Object detection refinement using Markov random field based pruning and learning based rescoring

Contextual information such as the co-occurrence of objects and the location of objects has played an important role in object detection. We present candidate pruning and object rescoring methods that leverage contextual information and that can improve the state-of-the-art CNN-based object detectio...

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Bibliographic Details
Main Authors: Inoue, Naoto, Furuta, Ryosuke, Yamasaki, Toshihiko, Aizawa, Kiyoharu
Format: Conference Proceeding
Language:English
Subjects:
Context
Context modeling
contextual model
Detectors
Fitting
MRF
Object detection
Support vector machines
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Summary:Contextual information such as the co-occurrence of objects and the location of objects has played an important role in object detection. We present candidate pruning and object rescoring methods that leverage contextual information and that can improve the state-of-the-art CNN-based object detection methods such as Fast R-CNN and Faster R-CNN. In our pruning method, we formulate candidate reduction as a Markov random field optimization problem. In our rescoring method, we employ a machine learning technique to reconsider the detection scores of candidate windows. We experimentally demonstrate improvements in R-CNN-based object detection methods using two datasets. Moreover, we apply our model to the structured retrieval task to show the potential applications of our model.
ISSN:2379-190X
DOI:10.1109/ICASSP.2017.7952437