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Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 7, Issue 4, Pages 1,470-1,492, DOI: https://doi.org/10.21307/ijssis-2017-716
License : (CC BY-NC-ND 4.0)
Received Date : 17-May-2014 / Accepted: 12-October-2014 / Published Online: 01-December-2014
In Bag of Words image presentation model, visual words are generated by unsupervised
clustering, which leaves out the spatial relations between words and results in such shorting comings as
limited semantic description and weak discrimination. To solve this problem, we propose to substitute
visual words by visual phrases in this article. Visual phrases built according to spatial relations between
words are semantic distrainable, and they can improve the accuracy of Bag of Words model.
Considering the traditional classification method based on Bag of Words model is vulnerable to the
background, block and scalar variance of an image, we propose in this article a multiple visual words
learning method for image classification, which is based on the concept of visual phrases combined
with Multiple Instance Learning. The final classification model is able to show the spatial features of
image classes. Experiments performed on standard image testing sets, Caltech 101 and Scene 15, show
the satisfying performance of this algorithm.
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