A novel web image retrieval method: bagging weighted hashing based on local structure information Online publication date: Wed, 04-Dec-2019
by Huanyu Li
International Journal of Grid and Utility Computing (IJGUC), Vol. 11, No. 1, 2020
Abstract: Hashing is widely used in ANN searching problems, especially in web image retrieval. An excellent hashing algorithm can help the users to search and retrieve their web images more conveniently, quickly and accurately. In order to conquer several deficiencies of ITQ in image retrieval problems, we use ensemble learning to solve them. An elastic ensemble framework has been proposed to guide the hashing design, and three important principles have been proposed, named high precision, high diversity, and optimal weight prediction. Based on this, we design a novel hashing method called BWLH. In BWLH, first, the local structure information of the original data is extracted to construct the local structure data, thus to improve the similarity-preserve ability of hash bits. Second, a weighted matrix is used to balance the variance of different bits. Third, bagging is exploited to expand diversity in different hash tables. Sufficient experiments show that BWLH can handle image retrieval problems effectively, and perform better than several state-of-the-art methods at same hash code length on dataset CIFAR-10 and LabelMe. Finally, 'search by image', a web-based use-case scenario of the proposed hashing BWLH is given to detail how the proposed method can be used in a web-based environment.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Grid and Utility Computing (IJGUC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com