Abstract
Interpreting semantic image concepts via their dominant compounds is a promising approach to achieve effective image retrieval via keywords. In this paper, a novel framework is proposed by using the salient objects as the semantic building blocks for image concept interpretation. This novel framework includes: (a) using machine learning technique to achieve automatic detection of the salient objects; (b) using Gaussian mixture model for semantic image concept interpretation by exploring the quantitative relationship between the semantic image concepts and their dominant compounds, i.e., salient objects. Our broad experiments on natural images have obtained significant improvements on semantic image classification.
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Fan, J., Gao, Y., Luo, H., Xu, G. (2004). Salient Objects: Semantic Building Blocks for Image Concept Interpretation. In: Enser, P., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A.F., Smeulders, A.W.M. (eds) Image and Video Retrieval. CIVR 2004. Lecture Notes in Computer Science, vol 3115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27814-6_44
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DOI: https://doi.org/10.1007/978-3-540-27814-6_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22539-3
Online ISBN: 978-3-540-27814-6
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