Real-time object-to-features vectorisation via Siamese neural networks

Fedor Fedorenko; Sergey Usilin

doi:10.1117/12.2268703

17 March 2017 Real-time object-to-features vectorisation via Siamese neural networks

Fedor Fedorenko, Sergey Usilin

Proceedings Volume 10341, Ninth International Conference on Machine Vision (ICMV 2016); 103411R (2017) https://doi.org/10.1117/12.2268703
Event: Ninth International Conference on Machine Vision, 2016, Nice, France

Abstract

Object-to-features vectorisation is a hard problem to solve for objects that can be hard to distinguish. Siamese and Triplet neural networks are one of the more recent tools used for such task. However, most networks used are very deep networks that prove to be hard to compute in the Internet of Things setting. In this paper, a computationally efficient neural network is proposed for real-time object-to-features vectorisation into a Euclidean metric space. We use L₂ distance to reflect feature vector similarity during both training and testing. In this way, feature vectors we develop can be easily classified using K-Nearest Neighbours classifier. Such approach can be used to train networks to vectorise such “problematic” objects like images of human faces, keypoint image patches, like keypoints on Arctic maps and surrounding marine areas.

Citation Download Citation

Fedor Fedorenko and Sergey Usilin "Real-time object-to-features vectorisation via Siamese neural networks", Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103411R (17 March 2017); https://doi.org/10.1117/12.2268703

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