Image Classification Using Bag of Visual Words and Visual Saliency Model


KIPS Transactions on Software and Data Engineering, Vol. 3, No. 12, pp. 547-552, Dec. 2014
10.3745/KTSDE.2014.3.12.547, Full Text:

Abstract

As social multimedia sites are getting popular such as Flickr and Facebook, the amount of image information has been increasing very fast. So there have been many studies for accurate social image retrieval. Some of them were web image classification using semantic relations of image tags and BoVW(Bag of Visual Words). In this paper, we propose a method to detect salient region in images using GBVS(Graph Based Visual Saliency) model which can eliminate less important region like a background. First, We construct BoVW based on SIFT algorithm from the database of the preliminary retrieved images with semantically related tags. Second, detect salient region in test images using GBVS model. The result of image classification showed higher accuracy than the previous research. Therefore we expect that our method can classify a variety of images more accurately.


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Cite this article
[IEEE Style]
H. W. Jang and S. S. Cho, "Image Classification Using Bag of Visual Words and Visual Saliency Model," KIPS Transactions on Software and Data Engineering, vol. 3, no. 12, pp. 547-552, 2014. DOI: 10.3745/KTSDE.2014.3.12.547.

[ACM Style]
Hyun Woong Jang and Soo Sun Cho. 2014. Image Classification Using Bag of Visual Words and Visual Saliency Model. KIPS Transactions on Software and Data Engineering, 3, 12, (2014), 547-552. DOI: 10.3745/KTSDE.2014.3.12.547.