Abstract
The context of objects can provide auxiliary discrimination beyond objects. However, this effective information has not been fully explored. In this paper, we propose Tri-level Combination for Image Representation (TriCoIR) to solve the problem at three different levels: object intrinsic, strongly-related context and weakly-related context. Object intrinsic excludes external disturbances and more focuses on the objects themselves. Strongly-related context is cropped from the input image with a more loose bound to contain surrounding context. Weakly-related one is recovered from the image other than object for global context. First, strongly and weakly-related context are constructed from input images. Second, we make cascade transformations for more intrinsical object information, which depends on the consistency between generated global context and input images in the regions other than object. Finally, a joint representation is acquired based on these three level features. The experiments on two benchmark datasets prove the effectiveness of TriCoIR.
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Acknowledgement
This work is supported by National Basic Research Program of China (973 Program): 2012CB316400 and 2015CB351802, National Natural Science Foundation of China: 61303154 and 61332016, the Open Project of Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences.
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Li, R., Zhang, C., Huang, Q. (2016). Tri-level Combination for Image Representation. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9916. Springer, Cham. https://doi.org/10.1007/978-3-319-48890-5_25
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DOI: https://doi.org/10.1007/978-3-319-48890-5_25
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