Abstract
This paper presents a novel saliency detection approach using multiple features. There are three types of features to be extracted from a local region around each pixel, including intensity, color and orientation. Principal Component Analysis(PCA) is employed to reduce the dimension of the generated feature vector and kernel density estimation is used to measure saliency. We compare our method with five classical methods on a publicly available data set. Experiments on human eye fixation data demonstrate that our method performs better than other methods.
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He, X., Jing, H., Han, Q., Niu, X. (2012). A Novel Nonparametric Approach for Saliency Detection Using Multiple Features. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Intelligent Information and Database Systems. ACIIDS 2012. Lecture Notes in Computer Science(), vol 7197. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28490-8_9
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DOI: https://doi.org/10.1007/978-3-642-28490-8_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28489-2
Online ISBN: 978-3-642-28490-8
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