Annotation Based Image Retrieval using GMM and Spatial Related Object Approaches
Monica Hidajat · International Journal of Control and Automation · 2015
Image annotation and retrieval has been a popular research topic for decades.Based on published journals from 2012 until 2015, a lot of research and studies has been focused on Content Based Image Retrieval (CBIR).In most cases, CBIR systems that use an image as the input query always face a problem called semantic gap due to the use of low-level features for similarity matching.The semantic gap will consequently reduce the performance of the CBIR systems.To overcome this problem, a new class of CBIR known as Annotation Based Image Retrieval (ABIR) has been developed.The ABIR systems, based on the annotation process of the images, employ bag of words for query.However it is found that employing pure bag of word only is not adequate to solve semantic problem.In this study, an attempt to use a Gaussian Mixture Model (GMM) based approach and spatial related information of the annotated objects has been performed in order to improve the performance of the ABIR systems.From the retrieval experiments, it is found that ABIR could achieve average performance up to 88% which is two times better than the CBIR systems in reducing the semantic problem.