Data-Guided Image Retrieval System under Big Data Environment: Design and Implementation

DOI

10.2991/isrme-15.2015.110How to use a DOI?

Keywords

Image Retrieval System; Data Mining and Analysis; Image Representation; Big Data Environment; Saliency Map; Graph Learning.

Abstract

In Bag-of-Words based image retrieval, the SIFT visual word has a low discriminative power, so false positive matches occur prevalently. The research on data-guided image retrieval system is a hot topic. Its innovation is using PCNN and ICM in image feature extraction with translation, rotation, scale and distortion invariance and good resistance to noise, the PCNN and ICM extracted features as the image texture feature is applied to image retrieval system. The main idea is to use the PCNN and the ICM process images, get corresponds to different gray level values of binary image sequence, the sequence of the entropy of each image sequence, the one dimensional feature vector as the texture feature; Then using Euclidean distance similarity calculation. The experimental results show that the method not only has strong robustness to noise, at the same time can reduce eigenvector dimension, scale, translation and rotation invariance, and can get higher retrieval rate. In the future, we decide to do more comparison experiment to verify the effectiveness of the method.

Copyright

© 2015, 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  - Chao Huang
PY  - 2015/04
DA  - 2015/04
TI  - Data-Guided Image Retrieval System under Big Data Environment: Design and Implementation
BT  - Proceedings of the 2015 International Conference on Intelligent Systems Research and Mechatronics Engineering
PB  - Atlantis Press
SP  - 518
EP  - 523
SN  - 1951-6851
UR  - https://doi.org/10.2991/isrme-15.2015.110
DO  - 10.2991/isrme-15.2015.110
ID  - Huang2015/04
ER  -