Abstract
With the prevalence of smart embedded systems, the amount of images being captured and processed on mobile devices have grown significantly in recent years. Image feature descriptors which play crucial roles in detection or recognition tasks are expected to exhibit robust matching performance while at the same time maintain reasonable storage requirement. Among the local feature descriptors that have been proposed previously, local intensity order patterns (LIOP) demonstrated superior performance in many benchmark studies. As LIOP encodes the ranking relation in a point set (with N elements), however, its feature dimension increases drastically (N!) with the number of a neighboring sampling points around a pixel. To alleviate the dimensionality issue, this paper presents a local feature descriptor by considering pairwise intensity relation in a pixel group, thereby reducing feature dimension to the order of \(C^{N}_{2}\). In the proposed method, the threshold for assigning order relation is set dynamically according to local intensity distribution. Different weighting schemes, including linear transformation and Euclidean distance, have also been investigated to adjust the contribution of each pairing relation. Ultimately, dynamic local intensity order relations (DLIOR) pattern is devised to effectively encode intensity order relation of each pixel group. Experimental results indicate that DLIOR consumes less storage space than LIOP but achieves comparable or superior feature matching performance using benchmark data set.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, Z., Fan, B., Wu, F.: Local intensity order pattern for feature description. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 603–610. IEEE (2011)
Liao, W.-H., Wu, C.-C., Lin, M.-C.: Feature descriptor based on local intensity order relations of pixel group. In: 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE (2016)
Wang, Z., Fan, B., Wang, G., Wu, F.: Exploring local and overall ordinal information for robust feature description. IEEE Trans. Pattern Anal. Mach. Intell. 38(11), 2198–2211 (2016)
Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. Int. J. Comput. Vis. 60(1), 63–86 (2004)
Miksik, O., Mikolajczyk, K.: Evaluation of local detectors and descriptors for fast feature matching, In: 21st International Conference on Pattern Recognition (ICPR), pp. 2681–2684 (2012)
Acknowledgments
This work is supported by the Ministry of Science and Technology, Taiwan under Grants no. MOST-108-2634-F-004-001 through Pervasive Artificial Intelligence Research (PAIR) Labs.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Liao, WH., Yu, C., Wu, YC. (2019). Construction and Optimization of Feature Descriptor Based on Dynamic Local Intensity Order Relations of Pixel Group. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11662. Springer, Cham. https://doi.org/10.1007/978-3-030-27202-9_33
Download citation
DOI: https://doi.org/10.1007/978-3-030-27202-9_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27201-2
Online ISBN: 978-3-030-27202-9
eBook Packages: Computer ScienceComputer Science (R0)