Automatic Image Annotation Using CMRM with Scene Information
Abstract: Searching of digital
images in a disorganized image collection is a challenging problem. One step of
image searching is automatic image annotation. Automatic image annotation
refers to the process of automatically assigning relevant text keywords to any
given image, reflecting its content. In the past decade many automatic image
annotation methods have been proposed and achieved promising result. However,
annotation prediction from the methods is still far from accurate. To tackle
this problem, in this paper we propose an automatic annotation method using
relevance model and scene information. CMRM is one of automatic image
annotation method based on relevance model approach. CMRM method assumes that
regions in an image can be described using a small vocabulary of blobs. Blobs
are generated from segmentation, feature extraction, and clustering. Given a
training set of images with annotations, this method predicts the probability
of generating a word given the blobs in an image. To improve annotation
prediction accuracy of CMRM, in this paper we utilize scene information
incorporate with CMRM. Our proposed method is called scene-CMRM. Global image
region can be represented by features which indicate type of scene shown in the
image. Thus, annotation prediction of CMRM could be more accurate based on that
scene type. Our experiments showed that, the methods provides prediction with
better precision than CMRM does, where precision represents the percentage of
words that is correctly predicted.
Author: Julian Sahertian,
Saiful Akbar
Journal Code: jptkomputergg170175
