Volume local phase quantization for blur-insensitive dynamic texture classification

Authors:
Juhani Päivärinta;Esa Rahtu;Janne Heikkilä
Affiliations:
Department of Electrical and Information Engineering, University of Oulu, Finland;Department of Electrical and Information Engineering, University of Oulu, Finland;Department of Electrical and Information Engineering, University of Oulu, Finland
Venue:
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Year:
2011

Citing 7
Cited 2

Dynamic Textures

International Journal of Computer Vision
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns

IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions

IEEE Transactions on Pattern Analysis and Machine Intelligence
Blur Insensitive Texture Classification Using Local Phase Quantization

ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
DynTex: A comprehensive database of dynamic textures

Pattern Recognition Letters
Maximum margin distance learning for dynamic texture recognition

ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Dynamic texture recognition using normal flow and texture regularity

IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II

Directional space-time oriented gradients for 3d visual pattern analysis

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Multiple kernel learning for emotion recognition in the wild

Proceedings of the 15th ACM on International conference on multimodal interaction

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a blur-insensitive descriptor for dynamic textures. The Volume Local Phase Quantization (VLPQ) method introduced is based on binary encoding of the phase information of the local Fourier transform at low frequency points and is an extension to the LPQ operator used for spatial texture analysis. The local Fourier transform is computed efficiently using 1-D convolutions for each dimension in a 3-D volume. The data achieved is compressed to a smaller dimension before a scalar quantization procedure. Finally, a histogram of all binary codewords from dynamic texture is formed. The performance of VLPQ was evaluated both in the case of sharp dynamic textures and spatially blurred dynamic textures. Experiments on a dynamic texture database DynTex++ show that the new method tolerates more spatial blurring than LBP-TOP, which is a state-of-the-art descriptor, and its variant LPQ-TOP.