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.
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Nasierding, G., Kouzani, A.Z. (2010). Image to Text Translation by Multi-Label Classification. In: Huang, DS., Zhang, X., Reyes García, C.A., Zhang, L. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2010. Lecture Notes in Computer Science(), vol 6216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14932-0_31
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DOI: https://doi.org/10.1007/978-3-642-14932-0_31
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