Abstract
Visual features extraction for large-scale image retrieval is a challenging task. Bag-of-Features (BOF) is one of most popular models and gains attractive performance. However, BOF intrinsically represents an image as an unordered collection of local descriptors based on the intensity information, which provides little insight into the spatial structure of the image. This paper proposes a Spatial Weighting BOF (SWBOF) model to extract a new kind of bag-of-features by using spatial information, which is inspired by the idea that different parts of an image object play different roles on its categorization. Three approaches to measure the spatial information, local variance, local entropy and adjacent blocks distance are extensively studied, respectively. Experimental results show that SWBOF significantly improves the performance of the traditional BOF method, and achieves the best performance on the Corel database to our best knowledge.
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Wang, C., Zhang, B., Qin, Z., Xiong, J. (2013). Spatial Weighting for Bag-of-Features Based Image Retrieval. In: Qin, Z., Huynh, VN. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2013. Lecture Notes in Computer Science(), vol 8032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39515-4_8
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DOI: https://doi.org/10.1007/978-3-642-39515-4_8
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