Image to text translation by multi-label classification

Authors:
Gulisong Nasierding;Abbas Z. Kouzani
Affiliations:
Department of Computer Science, Xinjiang Normal University, Urumqi, P.R. China and School of Engineering, Deakin University, Geelong, VIC, Australia;School of Engineering, Deakin University, Geelong, VIC, Australia
Venue:
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Year:
2010

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Abstract

This paper presents an image to text translation platform consisting of image segmentation, region features extraction, region blobs clustering, and translation components. Different multi-label learning method is suggested for realizing the translation component. Empirical studies show that the predictive performance of the translation component is better than its counterparts when employed a dual-random ensemble multi-label classification algorithm that tested on the scene image dataset under all the selected evaluation criteria; while multi-label k-nearest neighbor learning algorithm performed nicely on jmlr2003 dataset. This achievement can facilitate formation of image to text translation and image annotation systems. The findings of this work suggest that different learning algorithms can be used for translating different type of images into text more effectively.