Abstract
In superpixel-based image parsing, the image is first segmented into visually consistent small regions, i.e. superpixels; then superpixels are parsed into different categories. SuperParsing algorithm provides an elegant nonparametric solution to this problem without any need for classifier training. Superpixels are labeled based on the likelihood ratios that are computed from class conditional density estimates of feature vectors. In this paper, local kernel density estimation is proposed to improve the estimation of likelihood ratios and hence the labeling accuracy. By optimizing kernel bandwidths for each feature vector, feature densities are better estimated especially when the set of training samples is sparse. The proposed method is tested on the SIFT Flow dataset consisting of 2,688 images and 33 labels, and is shown to outperform SuperParsing and some of its extended versions in terms of classification accuracy.
H.F. Ates—This work is supported in part by TUBITAK project no: 115E307 and by Isik University BAP project no: 14A205.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating color descriptors for object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1582–1596 (2010)
Fulkerson, B., Vedaldi, A., Soatto, S.: Class segmentation and object localization with superpixel neighborhoods. In: IEEE 12th International Conference Computer Vision I (ICCV), pp. 670–677 (2009)
Achanta, R., et al.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)
Vedaldi, A., Soatto, S.: Quick shift and kernel methods for mode seeking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 705–718. Springer, Heidelberg (2008)
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)
Kluckner, S., Donoser, M., Bischof, H.: Super-pixel class segmentation in large-scale aerial imagery. In: Proceedings of Annual Workshop Austrian Association for Pattern Recognition (2010)
Shotton, J., Johnson, M., Cipolla, R.: Semantic texton forests for image categorization and segmentation. In: IEEE Conference on Computer Vision Pattern Recognition (CVPR) (2008)
Gould, S., Fulton, R., Koller, D.: Decomposing a scene into geometric and semantically consistent regions. In: ICCV (2009)
Liu, C., et al.: SIFT flow: dense correspondence across difference scenes. In: ECCV (2008)
Tighe, J., Lazebnik, S.: SuperParsing: scalable nonparametric image parsing with superpixels. Int. J. Comput. Vis. 101(2), 329–349 (2013)
Eigen, D., Fergus, R.: Nonparametric image parsing using adaptive neighbor sets. In: CVPR, pp. 2799–2806 (2012)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: CVPR (2006)
Oliva, A., Torralba, A.: Building the gist of a scene: the role of global image features in recognition. Vis. Percept. Progress Brain Res. 155, 23–36 (2006)
Torralba, A., Fergus, R., Freeman, W.T.: 80 million tiny images: a large dataset for non-parametric object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 30, 1958–1970 (2008)
Shimazaki, H., Shinomoto, S.: Kernel bandwidth optimization in spike rate estimation. J. Comput. Neurosci. 29, 171–182 (2010)
Ak, K.E., Ates, H.F.: Scene segmentation and labeling using multi-hypothesis superpixels. In: Signal Processing and Communications Applications Conference (SIU), pp. 847–850 (2015)
George, M.: Image parsing with a wide range of classes and scene-level context. In: CVPR, pp. 3622–3630 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Ates, H.F., Sunetci, S., Ak, K.E. (2016). Kernel Likelihood Estimation for Superpixel Image Parsing. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-41501-7_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-41500-0
Online ISBN: 978-3-319-41501-7
eBook Packages: Computer ScienceComputer Science (R0)