Visual saliency detection based on modeling the spatial Gaussianity

Hongbin Ju

doi:10.1117/12.2182055

13 April 2015 Visual saliency detection based on modeling the spatial Gaussianity

Hongbin Ju

Proceedings Volume 9522, Selected Papers from Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics 2014, Part II; 95222N (2015) https://doi.org/10.1117/12.2182055
Event: Selected Proceedings of the Photoelectronic Technology Committee Conferences held August-October 2014, 2014, China, China

Abstract

In this paper, a novel salient object detection method based on modeling the spatial anomalies is presented. The proposed framework is inspired by the biological mechanism that human eyes are sensitive to the unusual and anomalous objects among complex background. It is supposed that a natural image can be seen as a combination of some similar or dissimilar basic patches, and there is a direct relationship between its saliency and anomaly. Some patches share high degree of similarity and have a vast number of quantity. They usually make up the background of an image. On the other hand, some patches present strong rarity and specificity. We name these patches “anomalies”. Generally, anomalous patch is a reflection of the edge or some special colors and textures in an image, and these pattern cannot be well “explained” by their surroundings. Human eyes show great interests in these anomalous patterns, and will automatically pick out the anomalous parts of an image as the salient regions. To better evaluate the anomaly degree of the basic patches and exploit their nonlinear statistical characteristics, a multivariate Gaussian distribution saliency evaluation model is proposed. In this way, objects with anomalous patterns usually appear as the outliers in the Gaussian distribution, and we identify these anomalous objects as salient ones. Experiments are conducted on the well-known MSRA saliency detection dataset. Compared with other recent developed visual saliency detection methods, our method suggests significant advantages.

Citation Download Citation

Hongbin Ju "Visual saliency detection based on modeling the spatial Gaussianity", Proc. SPIE 9522, Selected Papers from Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics 2014, Part II, 95222N (13 April 2015); https://doi.org/10.1117/12.2182055

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KEYWORDS

Visualization

RGB color model

Visual process modeling

Eye models

Detector development

Feature extraction

Image compression

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