Random forest-based active learning for content-based image retrieval

被引：0

作者：

Bhosle N. ^{[1
]}

Kokare M. ^{[2
]}

机构：

[1] Department of Electronics and Telecommunication Engineering, D.Y. Patil College of Engineering, Ambi, Pune

[2] Department of Electronics and Telecommunication Engineering, S.G.G.S. Institute of Engineering and Technology, Vishnupuri, Nanded

来源：

Bhosle, Nilesh (bhoslenp@gmail.com) | 1600年 / Inderscience Publishers, 29, route de Pre-Bois, Case Postale 856, CH-1215 Geneva 15, CH-1215, Switzerland卷 / 13期

关键词：

Active learning; CBIR; Content-based image retrieval; Feature reweighting; Information retrieval; Random forest learning; Relevance feedback; Semantic gap;

D O I：

10.1504/IJIIDS.2020.108223

中图分类号：

学科分类号：

摘要：

The classification-based relevance feedback approach suffers from the problem of imbalanced training dataset, which causes instability and degradation in the retrieval results. In order to tackle with this problem, a novel active learning approach based on random forest classifier and feature reweighting technique is proposed in this paper. Initially, a random forest classifier is used to learn the user's retrieval intention. Then, in active learning the most informative classified samples are selected for manual labelling and added in training dataset, for retraining the classifier. Also, a feature reweighting technique based on Hebbian learning is embedded in the retrieval loop to find the weights of most perceptive features used for image representation. These techniques are combined together to form a hypothesised solution for the image retrieval problem. The experimental evaluation of the proposed system is carried out on two different databases and shows a noteworthy enhancement in retrieval results. Copyright © 2020 Inderscience Enterprises Ltd.

引用

页码：72 / 88

页数：16

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