Semantic image retrieval using random forest-based AdaBoost learning

Autor:	P. J. Deore, Vijay Shrinath Patil
Rok vydání:	2019
Předmět:	Majority rule Point (typography) business.industry Computer science Relevance feedback Content-based image retrieval Machine learning computer.software_genre Random forest Artificial intelligence AdaBoost business Image retrieval computer Information Systems Semantic gap
Zdroj:	International Journal of Intelligent Information and Database Systems. 12:229
ISSN:	1751-5866 1751-5858
DOI:	10.1504/ijiids.2019.10024514
Popis:	Efficient image retrieval from a large image repository is still a challenging task because of the semantic gap. In this paper, a stride is made towards reducing the semantic gap by proposing an efficient approach using relevance feedback and random forest-based AdaBoost learning. Initially, user feedback is used to move the query point more towards the relevant images. The user feedback is also used to train the random forest classifier, for learning the user retrieval intention. Then AdaBoost learning is used to identify the weak classifiers and to assign more weights to weak classifiers in the weighted majority voting scheme. AdaBoost learning is adopted to overcome the prediction variance of the random forest classifier. The experimental evaluation is performed on two different real world image databases and shows that the proposed approach is more efficient, as an average precision of 95% is achieved in six iterations of relevance feedback.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a88d7de7734f3c775b22f8a49bee9474 https://doi.org/10.1504/ijiids.2019.10024514 Zobrazit plný text záznamu