Abstract
The gap between human semantic perception of an image and its abstraction by some low-level features is one of the main shortcomings of the actual content-based image retrieval (CBIR) systems. This paper presents an effort to overcome this drawback and proposes a CBIR approach in which retrieved images are more likely to satisfy the user expectations. Multi-label classification, in which an instance is assigned to different classes, provides a general framework to establish semantic correspondence between images in a database and a query image. Accordingly, in the framework of multi-label classification, a new feature fusion strategy is developed based on Dempster–Shafer evidence theory to allow handling lack of prior probabilities and uncertain information provided by low-level features. In this study, texture features are extracted through wavelet correlogram and color features are obtained using correlogram of vector quantized image colors. These features are subsequently fused via a possibility approach for being used in multi-label classification to retrieve images relevant to a query image. Experimental results on three well-known public and international image datasets demonstrate the superiority of the proposed algorithm over its close counterparts in terms of average precision and F1 measure.
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Abrishami Moghaddam, H., Ghodratnama, S. Toward semantic content-based image retrieval using Dempster–Shafer theory in multi-label classification framework. Int J Multimed Info Retr 6, 317–326 (2017). https://doi.org/10.1007/s13735-017-0134-y
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DOI: https://doi.org/10.1007/s13735-017-0134-y