Combining firefly algorithm and Bayesian classifier: new direction for automatic multilabel image annotation. Issue 10 (1st October 2016)

• Record Type: Journal Article
• Authors: Darwish, Saad M.
• Is Part Of: IET image processing. Volume 10:Issue 10(2016)
• Page Start: 763
• Page End: 772
• Publication Date: 2016-10-01
• Subjects: Bayes methods -- learning (artificial intelligence) -- pattern classification -- image segmentation -- image recognition -- feature extraction; automatic multilabel image annotation -- firefly algorithm -- digital images -- machine learning framework -- huge intraconcept visual similarity issue -- huge intraconcept visual diversity issue -- interrelated nonparametric Bayesian classifier training framework -- over‐segmented region -- Otsu method segmentation algorithm -- FA -- maximum variance intracluster -- feature extraction -- Corel benchmark image dataset -- label ranking problem -- segment recognition; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2015.0492
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : As digital images are increasing exponentially; it is very attractive to develop more effective machine learning frameworks for automatic image annotation. In order to address the most prominent issues (huge inter‐concept visual similarity and huge intra‐concept visual diversity) more effectively, an inter‐related non‐parametric Bayesian classifier training framework to support multi‐label image annotation is developed. For this purpose, an image is viewed as a bag, and its instances are the over‐segmented regions within it found automatically with an adopted Otsu's method segmentation algorithm. Here firefly algorithm (FA) is utilised to enhance Otsu's method in the direction of finding optimal multilevel thresholds using the maximum variance intra‐clusters. FA has high convergence speed and less computation rate as compared with some evolutionary algorithms. By generating blobs, the extracted features for segmented regions, the concepts which are learned by the classifier tend to relate textually to the words which occur most often in the data and visually to the easiest to recognise segments. This allowing the opportunity to assign a word to each object (localised labelling). Extensive experiments on Corel benchmark image datasets will validate the effectiveness of the proposed solution to multi‐label image annotation and label ranking problem.