@inproceedings{fad736c30ae04f50b54af243d0fcf901,
title = "Tensor Decomposition of Dense SIFT Descriptors in Object Recognition",
abstract = "In machine vision, Scale-invariant feature transform (SIFT) and its variants have been widely used in image classification task. However, the high dimensionality nature of SIFT features, usually in the order of multiple thousands per image, would require careful consideration in place to achieve accurate and timely categorization of objects within images. This paper explores the possibility of processing SIFT features as tensors and uses tensor decomposition techniques on high-order SIFT tensors for dimensionality reduction. The method focuses on both accuracy and efficiency aspects and the validation result with the Caltech 101 dataset confirms the improvement with notable margins.",
keywords = "Tensor-Decomposition, SIFT Descriptors, Object Recognition",
author = "Tan Vo and Dat TRAN and Wanli MA",
year = "2014",
month = apr,
day = "23",
language = "English",
isbn = "9782874190957",
volume = "1",
series = "22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings",
publisher = "European Symposium of Artificial Neural Networks",
pages = "319--324",
editor = "Michel Verleysen",
booktitle = "ESANN 2014 European Symposium of Artificial Neural Networks, Computational Intelligence and Machine Learning",
note = "European Symposium on Artificial Neural Networks 2014 : ESANN 2014, ESANN 2014 ; Conference date: 23-04-2014 Through 25-04-2014",
}