A New VLAD Method with Dense SIFT Selection Application in Image Classification
Zhi Qian, Qijun Hong, Gang Huang, Pingping Liu, Yuanjie Yan, Min Xie
Available Online June 2017.
- https://doi.org/10.2991/caai-17.2017.127How to use a DOI?
- image classification; features selection; SLIC; VLAD(key words)
- Since Dense SIFT causes a long time spending during clustering due to an excessive order of magnitude, and its feature descriptors reserve excessive insignificant features, we present a new method that using SLIC to select descriptors to address this problem. Furthermore, when VLAD aggregates, the partial directions of feature vectors have the excessive data offset and still distorts after the dimension deduction treatment. Regarding such issue, the algorithm that possesses the optimized clustering descriptor with feature membership information called FS-VLAD is proposed. The algorithm adopts the principle of the fuzzy cost function with the smallest deviation regarding the quadratic sum of the neighbor clustering center to calculate the feature membership degree. After conducting classification test, the result demonstrates that in comparison with the mainstream Dense SIFT + VLAD classification model, the new methods could improve by around 15%, and possesses better generality.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Zhi Qian AU - Qijun Hong AU - Gang Huang AU - Pingping Liu AU - Yuanjie Yan AU - Min Xie PY - 2017/06 DA - 2017/06 TI - A New VLAD Method with Dense SIFT Selection Application in Image Classification BT - 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017) PB - Atlantis Press SP - 568 EP - 574 SN - 1951-6851 UR - https://doi.org/10.2991/caai-17.2017.127 DO - https://doi.org/10.2991/caai-17.2017.127 ID - Qian2017/06 ER -