Rotation invariant curvelet based image retrieval & classification via Gaussian mixture model and co-occurrence features
Küçük Resim Yok
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer New York LLC
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Demand for better retrieval methods continue to outstrip the capabilities of available technologies despite the rapid growth of new feature extraction techniques. Extracting discriminatory features that contain texture specific information are of crucial importance in image indexing. This paper presents a novel rotation invariant texture representation model based on the multi-resolution curvelet transform via co-occurrence and Gaussian mixture features for image retrieval and classification. To extract these features, curvelet transform is applied and the coefficients are obtained at each scale and orientation. The Gaussian mixture model (GMM) features are computed from each of the sub bands and co-occurrence features are computed for only specific sub band. Rotation invariance is provided by applying cycle-shift around the GMM features. The proposed method is evaluated on well-known databases such as Brodatz, Outex_TC_00010, Outex_TC_00012, Outex_TC_00012horizon, Outex_TC_00012tl84, Vistex and KTH-TIPS. When the feature vector is analyzed in terms of its size, it is observed that its dimension is smaller than that of the existing rotation-invariant variants and it has a very good performance. Simulation results show a good performance achieved by combining different techniques with the curvelet transform. Proposed method results in high degree of success rate in classification and in precision-recall value for retrieval. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.
Açıklama
Anahtar Kelimeler
Curvelet transform, Image classification, Image retrieval, Classification (of information), Gaussian distribution, Image classification, Image texture, Rotation, Co-occurrence features, Curve-let transforms, Feature extraction techniques, Gaussian Mixture Model, Rotation invariance, Rotation invariant, Specific information, Texture representation, Image retrieval, Curvelet transform, Image classification, Image retrieval, Classification (of information), Gaussian distribution, Image classification, Image texture, Rotation, Co-occurrence features, Curve-let transforms, Feature extraction techniques, Gaussian Mixture Model, Rotation invariance, Rotation invariant, Specific information, Texture representation, Image retrieval
Kaynak
Multimedia Tools and Applications
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
