Rotation and Scale Invariant Texture Analysis with Tunable Gabor Filter Banks

Transcription

1 Rotation and Scale Invariant Texture Analysis with Tunable Gabor Filter Banks Xinqi Chu Kap Luk Chan School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore { chux0001, eklchan }@ntu.edu.sg Jan. 14, 2009

2 The Invariance Issue on Textures Why Invariance? D001 D004 D021 D040 Original: Rotation: Scaling: Concurrent Rotation and Scaling: Examples illustrating the invariance problem

3 The Invariance Issue on Textures Classification of Existing Invariant Texture Recognition Approaches Same size, Rotation invariance Rotation or Concurrent Rotation & same orientation max. 40 textures Scale invariance Scale invariance B.S. Manjunath & W.Y. Ma Porter & Canagarajah Kai-Kuang Ma Ju Han C.M Pun & M.C Lee on 112 textures Discrete Wavelet Decomposition on 16 textures on 25 textures Kim and Udpa Cohen and Patel Rotated wavelet filters on 9 textures Fountain and Tan Chen and Kundu Multichannel Filtering on 10 textures Kashyap and Khotanzad Wu and Wei Circular autoregressive model on 10 textures Our method is based on a spectral shift measure with a fast search strategy which has the same recognition speed as in the circumstance where rotation and scale are absent. The method is tested on 112 textures

4 Concurrent Rotation and Scale Texture Recognition The most desirable methods are expected to have the following properties: Concurrent Rotation and Scaling Invariance Good recognition performance when large number of texture-classes present We chose Manjunath s Gabor filter banks(non-invariance) to build on top of it because: It shows excellent performance on the entire Brodatz database consist of 112 classes It s mimicry to receptive field in eye cortex. Therefore our aim is to achieve"concurrent rotation and scaling invariance".

5 The 2-D Gabor Function The Band-pass Filter Figure: 2DGabor

6 The 2-D Gabor filter bank Figure: The 2-D Gabor filter bank

7 The 2-D Gabor filter bank An example from the Brodatz Database original128x128/d004.bmp

8 The Effects of Rotation and Scale How to estimate rotation and scaling Problem to solve: Classifying a input texture image Unknown Class ID Unknown Rotation(to the true reference) Unknown Scale(to the true reference) We first assume that the class ID for the input texture is already known. Therefore we focus on the estimation of rotation and scale first. We ll tackle the entire problem after the estimation method is introduced.

9 The Effects of Rotation and Scale How to estimate rotation and scaling when the CLASS ID IS KNOWN? Original 20 degree counter-clockwise 1.5 up-scaled 1.5 up-scaled 20 degree counter clockwise Texture 21 Cartisan Frequency domain: Dominant Peak Position in Polar Coordinates: rho=35, theta1.57 rho=34.78, theta=1.89 rho=24, theta1.57 rho=23.14, theta1.89 Estimation: N/A(reference) s=1, r=18.43 degree s=1.46, r=0 degree s=1.5125, r=18.43 degree Error: N/A 7.8% in r 2.6% in s, 0.8% in s, 7.8% in r

10 The Idea and the Paradox We can measure the scale and rotation with respect to the true reference texture by a spectral shift measure in polar-frequency domain if the CLASS ID IS KNOWN. The chicken and egg problem: We have to know the correct CLASS ID in order to get the correct rotation and scale parameters. If we know the class ID for the input already, the classification job is done, why bother estimating these parameters? Input Texture Classification?? Rotation and scale Estimation Figure: The chicken and egg problem

11 The Solution to the Paradox The Algorithm Chart Training 112 training images 1 for each class Classification Input Texture 112 Spectrums 1 for each class Extract feature vectors for 112 reference images, 1 for each class Extract feature w.r.t 112 Gabor banks (Each feature is 48D) Compare with 112 reference texture feature vectors Locate the dominate frequency Estimate the position of dominant frequency location Classification Rotation and scale Estimation Figure: The solution to the chicken and egg problem

12 The Algorithm The advantage of this algorithm: Robustness: The only one method that has since applied to all 112 classes. The same time the input texture is classified, the rotation and scaling are also quantitatively evaluated, a property most of the method using the invariant representations do not have. Recognition Speed is a bit slower than the non-invariant algorithm(if implemented in Matlab). The disadvantage of this algorithm: Memory required increased to 128 times the original method. Inherent system errors.

13 The Evaluation of the Method The texture taxonomy Textures Inhomogenous Homogeneous Periodical Directional Random Texture Texture Spectrum Spectrum Other examples in the same category Other examples in the same category

14 The Evaluation of the Method (cont d) The experimental setup Scale: , step= 0.1, 8 scales Rotation: 0 o 180 o, step= 20 o, 10 rotations Totally = 8960 testing images. We have 80 images for each class in which 63 images are concurrently rotated and scaled. Figure: Each column represents two images from the same texture (from left to right:d18,d26,d27,d87,d112) but of 4 times scale difference, and you can observe that the texture is entirely different though the upper row is just a 4 up-scaled version of the lower row.

15 The Evaluation of the Method (cont d) The experimental results Conventional method Conventional method Proposed method no-rotation/scale rotated & scaled rotated & scaled 1 Manjunath s Applied on original dataset (128x128, 112x16) 1 Manjunath s Applied on rotated dataset(90x90, 112x16x9)

16 Methods Datasets Inhomo. Periodic Directional Rand. Overall Conventional no-rotation/scale Conventional rotated & scaled Our method rotated & scaled Rotation/scale effects on conventional Rate increase due to tuning

17 Summary and the way ahead Future work: Is mean and variance proper statistics for the filtered outputs? Dominant peak shift is probably not a accurate and safe measure of the rotation, we can take multiple peaks into consideration to get a more robust estimate. Inhomogeneous textures can be taken into account

18 Thank you!