Kernel likelihood estimation for superpixel image parsing

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Küçük Resim

Tarih

2016

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Springer Verlag

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Araştırma projeleri

Organizasyon Birimleri

Dergi sayısı

Özet

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.

Açıklama

Anahtar Kelimeler

Image parsing, Image segmentation, Kernel density estimation, Superpixel, Classification (of information), Image analysis, Image processing, Pixels, Statistics, Classification accuracy, Classifier training, Conditional density, Labeling accuracies, Likelihood estimation, Super pixels

Kaynak

Lecture Notes in Computer Science

WoS Q Değeri

Q4

Scopus Q Değeri

Q3

Cilt

9730

Sayı

Künye

Ateş, H. F., Sünetci, S. & Ak, K. E. (2016). Kernel likelihood estimation for superpixel image parsing. Paper presented at the Lecture Notes in Computer Science, 9730, 234-242. doi:10.1007/978-3-319-41501-7_27