an article by Ali Fakhari and Amir Masoud Eftekhari Moghadam (Islamic Azad University, Qazvin, Iran) published in Applied Soft Computing Volume 13 Issue 2 (February 2013)
Abstract
This paper proposes a semantic-based image retrieval approach which refers to the ability of using keywords for searching within image datasets.
This is possible by adding some textual metadata, called image annotation.
Combination of classification and regression in decision tree (DT) has been employed for multi-labeling image annotation in which, more than one label will be considered for every single tuple.
In the proposed approach, all concepts and their corresponding ranks will be stored in each DT leaf node instead of storing only a concept or a rank.
We have used a hierarchical network of semantics to achieve a better performance.
The main idea behind our approach is that in each leaf node, the system should give a higher rank to concepts with highest degree of purity and details according to prepared hierarchical semantic network.
A segmented, feature extracted and annotated image dataset, SAIAPR-TC12, has been used for evaluation.
A hierarchy of 256 semantic concepts which have been used in annotation process, made it very suitable for testing the approach.
Experimental results confirmed that our approach illustrates better performance in comparison with single-labelling approaches which only assign one class to every single tuple and only support linear relationship among concepts.
Graphical abstract
and other illustrations and figures from this paper [NB: These are worth looking at even if you only understood one word in three of the abstract!!]
Highlights
► The evaluation method has been changed to 4-fold cross validation.
► F-Measure has been added as another measure for performance evaluation.
► Some grammar errors have been fixed.
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