Modified one-class support vector machine for content-based image retrieval with relevance feedback
Image retrieval via traditional Content-Based Image Retrieval (CBIR) often incurs the semantic gap problem—non-correlation of image retrieval results with human semantic interpretation of images. In this paper, Relevance Feedback (RF) mechanism was incorporated into a traditional Query by Visual Exa...
Main Authors: | Oluwole A. Adegbola, David O. Aborisade, Segun I. Popoola, Olatide A. Amole, Aderemi A. Atayero |
---|---|
Format: | Article |
Language: | English |
Published: |
Taylor & Francis Group
2018-01-01
|
Series: | Cogent Engineering |
Subjects: | |
Online Access: | http://dx.doi.org/10.1080/23311916.2018.1541702 |
Similar Items
-
Salton and Buckley’s Landmark Research in Experimental Text Information Retrieval
by: Christine F. Marton
Published: (2011-12-01) -
Implementing Relevance Feedback for Content-Based Medical Image Retrieval
by: Ali Ahmed
Published: (2020-01-01) -
An Improved Retrievability-Based Cluster-Resampling Approach for Pseudo Relevance Feedback
by: Shariq Bashir
Published: (2016-11-01) -
Relevance Feedback in Content Based Image Retrieval: A Review
by: Manesh B. Kokare, et al.
Published: (2011-01-01) -
Interactive Content Based Image Retrieval using Multiuser Feedback
by: M. Premkumar, et al.
Published: (2017-12-01)