An Improved SIFT Algorithm Based on Canny Operator and Hillbert - Huang Transform

DOI

10.2991/ceie-16.2017.22How to use a DOI?

Keywords

Canny Operator; Hillbert - Huang Transform; Improved SIFT(Scale Invariant Feature Transform) Algorithm

Abstract

Latent attribute prediction problem in social network provides a set of conditions for the construction of text classification models. The general framework of current latent attribute prediction problems is mapping the text in social network to vector space, along with a classification model to classify different categories. Unfortunately, as the vector space model ignores the similarity and relevance between different words, it fails to identify the semantic fuzziness in natural language and always performs badly on a long text. With the aim of finding a better framework for age prediction problem, in this paper we propose a word embedding based tensor space model which maps text to tensor feature space. The proposed method relies on supervised tensor learning algorithms which are well studied by many scholars, thus allowing for its easy application in text classification problems. Two experiments on different testing sets show the effectiveness and limitation of our approach.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Dengshan Huang
AU  - Xiaonan Wang
AU  - Qi Qin
PY  - 2016/10
DA  - 2016/10
TI  - An Improved SIFT Algorithm Based on Canny Operator and Hillbert - Huang Transform
BT  - Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016)
PB  - Atlantis Press
SP  - 158
EP  - 165
SN  - 2352-5401
UR  - https://doi.org/10.2991/ceie-16.2017.22
DO  - 10.2991/ceie-16.2017.22
ID  - Huang2016/10
ER  -