Improve VLAD Using the Entropy Produced by BOW
- Hongwei Zhao, Yeran Wang, Pingping Liu, Chaoran Zhao, Xiang Li
- Corresponding Author
- Hongwei Zhao
Available Online June 2017.
- https://doi.org/10.2991/ammee-17.2017.135How to use a DOI?
- Image retrieval, VLAD, BOW, entropy.
- VLAD (vector of locally aggregated descriptors) is a type of global features extracted from the image, which is always used in image retrieval. Although VLAD is effective, it still needs to improve. VLAD is obtained by accumulating residuals, ignoring the number information of descriptors in a cluster. BOW (bag of words) is also a type of features, which describe the amounts of the descriptors within a cluster. In this paper, the concept of the quantity entropy is proposed based on the BOW method. The amount of descriptors is processed by the calculation steps of entropy to obtain the quantity entropy, and we add the quantity entropy to VLAD, which makes the image retrieval ability of VLAD improved.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Hongwei Zhao AU - Yeran Wang AU - Pingping Liu AU - Chaoran Zhao AU - Xiang Li PY - 2017/06 DA - 2017/06 TI - Improve VLAD Using the Entropy Produced by BOW BT - Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017) PB - Atlantis Press SN - 2352-5401 UR - https://doi.org/10.2991/ammee-17.2017.135 DO - https://doi.org/10.2991/ammee-17.2017.135 ID - Zhao2017/06 ER -