Region proposal-based semantic matcher

Anastasiia Moiseenko; Yuri Vizilter; Boris Vishnyakov; Vladimir Gorbatsevich; Oleg Vygolov

doi:10.1117/12.2525233

21 June 2019 Region proposal-based semantic matcher

Anastasiia Moiseenko, Yuri Vizilter, Boris Vishnyakov, Vladimir Gorbatsevich, Oleg Vygolov

Proceedings Volume 11061, Automated Visual Inspection and Machine Vision III; 1106109 (2019) https://doi.org/10.1117/12.2525233
Event: SPIE Optical Metrology, 2019, Munich, Germany

Abstract

Unseen object detection problem is known as a semantic matching problem. Thus, a semantic matcher takes two images as an input – the request image and the test image. The request image represents an object class needed to be found on the test image. In this paper, we propose a new region proposal based semantic matcher. In our region based semantic matcher we use the same ideas as in R-CNN. Our Body CNN also generates proposals similar to classical Faster R-CNN, and Head-CNN compares proposals with a request descriptor, extracted from the request image. To extract features from the request image we use Request descriptor CNN. All three CNNs – Head, Body and Request descriptor are trained together, end-to-end for seen class object detection by request and then applied to both seen and unseen classes. We have trained and tested our CNN on Pascal VOC Dataset.

Citation Download Citation

Anastasiia Moiseenko, Yuri Vizilter, Boris Vishnyakov, Vladimir Gorbatsevich, and Oleg Vygolov "Region proposal-based semantic matcher", Proc. SPIE 11061, Automated Visual Inspection and Machine Vision III, 1106109 (21 June 2019); https://doi.org/10.1117/12.2525233

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KEYWORDS

Sensors

Detection and tracking algorithms

Head

Image retrieval

Convolutional neural networks

Classification systems

Computer vision technology

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