Labeling scene objects is an essential task for many computer vision applications. However, differentiating scene objects with visual similarity is a very challenging task. To overcome this challenge, this paper proposes a position gradient and plane consistency based feature which is designed to distinguish visually similar objects and improve the overall labeling accuracy. Using the proposed feature we can differentiate objects with the same histogram of the gradient as well as we can differentiate horizontal and vertical objects. Integrating the proposed feature with low-level texture features and a neural network classifier, we achieve a superior performance (82%) compared to state-of-the-art scene labeling methods on the Stanford background dataset.
CITATION STYLE
Chowdhury, S., Verma, B., & Zhang, L. (2016). Position gradient and plane consistency based feature extraction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9948 LNCS, pp. 673–681). Springer Verlag. https://doi.org/10.1007/978-3-319-46672-9_75
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