Learning how to extract rotation-invariant and scale-invariant features from texture images

DOI10.1155/2008/691924zbMath1184.94046DBLPjournals/ejasp/Montoya-ZegarraPLTF08OpenAlexW2067016264WikidataQ59216137 ScholiaQ59216137MaRDI QIDQ966847

Neucimar J. Leite, Javier A. Montoya-Zegarra, João Paulo Papa, Ricardo da Silva Torres, Alexandre Xavier Falcão

Publication date: 24 April 2010

Published in: EURASIP Journal on Advances in Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2008/691924

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (2)

A multi-scale and multi-orientation image retrieval method based on rotation-invariant texture features ⋮ A Learning Algorithm for the Optimum-Path Forest Classifier

Uses Software

• LIBSVM
• Steerable pyramid

Cites Work

• A survey of content-based image retrieval with high-level semantics
• An evolutionary system for near-regular texture synthesis
• Texton gradients: The texton theory revisited
• A parametric texture model based on joint statistics of complex wavelet coefficients
• Attentive texture similarity as a categorization task: Comparing texture synthesis models
• Rotation-invariant texture classification
• Relative Fuzzy Connectedness among Multiple Objects: Theory, Algorithms, and Applications in Image Segmentation

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