Spatial weighting for bag-of-features based image retrieval

Authors:
Chuanqian Wang;Baochang Zhang;Zengchang Qin;Junyi Xiong
Affiliations:
Science and Technology on Aircraft Control Lab School of Automation Science and Electrical Engineering, Beihang University, Beijing, China;Science and Technology on Aircraft Control Lab School of Automation Science and Electrical Engineering, Beihang University, Beijing, China;Science and Technology on Aircraft Control Lab School of Automation Science and Electrical Engineering, Beihang University, Beijing, China,Intelligent Computing and Machine Learning Lab School of ...;Science and Technology on Aircraft Control Lab School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
Venue:
IUKM'13 Proceedings of the 2013 international conference on Integrated Uncertainty in Knowledge Modelling and Decision Making
Year:
2013

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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.