Decision fusion-based approach for content-based image classification

作     者：Sudeep Thepade Rik Das Saurav Ghosh 

作者机构：Department of Information TechnologyPimpri Chinchwad College of EngineeringPuneIndia Department of Information TechnologyXavier Institute of Social ServiceRanchiIndia A.K.Choudhury School of Information TechnologyUniversity of CalcuttaKolkataIndia 

出 版 物：《International Journal of Intelligent Computing and Cybernetics》 (智能计算与控制论国际期刊（英文）)

年 卷 期：2017年第10卷第3期

页      面：310-331页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Classification Information fusion Content-based feature extraction Retrieval 

摘      要：Purpose–Current practices in data classification and retrieval have experienced a surge in the use of multimedia *** of desired information from the huge image databases has been facing increased complexities for designing an efficient feature extraction *** approaches of image classification with text-based image annotation have faced assorted limitations due to erroneous interpretation of vocabulary and huge time consumption involved due to manual ***-based image recognition has emerged as an alternative to combat the aforesaid ***,exploring rich feature content in an image with a single technique has lesser probability of extract meaningful signatures compared to multi-technique feature ***,the purpose of this paper is to explore the possibilities of enhanced content-based image recognition by fusion of classification decision obtained using diverse feature extraction ***/methodology/approach–Three novel techniques of feature extraction have been introduced in this paper and have been tested with four different classifiers *** four classifiers used for performance testing were K nearest neighbor(KNN)classifier,RIDOR classifier,artificial neural network classifier and support vector machine ***,classification decisions obtained using KNN classifier for different feature extraction techniques have been integrated by Z-score normalization and feature scaling to create fusion-based framework of image *** has been followed by the introduction of a fusion-based retrieval model to validate the retrieval performance with classified *** works on content-based image identification have adopted fusion-based ***,to the best of the authors’knowledge,fusion-based query classification has been addressed for the first time as a precursor of retrieval in this ***–The proposed fusion techniques have successfull