Random forest-based active learning for content-based image retrieval. (26th June 2020)

• Record Type: Journal Article
• Authors: Bhosle, Nilesh; Kokare, Manesh
• Is Part Of: International journal of intelligent information and database systems. Volume 13:Number 1(2020)
• Page Start: 72
• Page End: 88
• Publication Date: 2020-06-26
• Subjects: content-based image retrieval -- CBIR -- relevance feedback -- random forest learning -- active learning -- semantic gap -- feature reweighting -- information retrieval; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

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.