Rotation-Invariant Texture Image Retrieval Using Rotated Complex Wavelet Filters

Kokare, Manesh; Biswas, Prabir Kumar; Chatterji, B. N.

doi:10.1109/tsmcb.2006.874692

Cited by 121 publications

(58 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fuzzy sets with Gaussian membership functions are designed for the classification of input variables. These fuzzy sets can be defined using the following equation [15].…”

Section: Fuzzy Logic Classifiermentioning

confidence: 99%

Identification of Metallurgical Surface Finish Images-In Manufacturing Process using Fuzzy Classifier

Mukane¹,

Shaikh²

2013

IJCA

View full text Add to dashboard Cite

Surface metrology with image processing is a challenging task having wide application in the industry. Analysis of surface finish can be done using image processing. The proposed system aims at identifying three classes of surface finish images, viz. Casting, Milling and shaping. The system proposes an effective combination of features for analysis of the engineering surfaces. The prototype system developed for classification of surface finish images using Discrete Wavelet Transform (DWT) and Fuzzy Logic classifier. The feature extracted using DWT are Standard Deviation and Mean. The system out performs the earlier methods and gives 92.78% of average success rate for only 72 number of features. The system is also analysed by different wavelet filters for maximum success rate and minimum success rate comparison. General TermsSurface finish identification of metallurgical images, Texture Analysis and Fuzzy logic.

show abstract

“…Fuzzy sets with Gaussian membership functions are designed for the classification of input variables. These fuzzy sets can be defined using the following equation [15].…”

Section: Fuzzy Logic Classifiermentioning

confidence: 99%

Identification of Metallurgical Surface Finish Images-In Manufacturing Process using Fuzzy Classifier

Mukane¹,

Shaikh²

2013

IJCA

View full text Add to dashboard Cite

show abstract

“…Since recognizing texture irrespective of its orientation is a very important issue, many efforts have been devoted in literature to rotation-invariant texture recognition [8][9][10][11] [17][18][19][20][21][22][23][24], such as those based on rotated wavelet filter (RWF) [8] and complex wavelet transform (CWT) [9]. But most of these methods are aimed at modifying the wavelet filter or transform to adapt to the rotation of the texture (i.e., trying to extract rotation-invariant features), without manipulating the rotated texture image itself.…”

Section: Related Workmentioning

confidence: 99%

Rotation-invariant texture recognition by rotation compensation and wavelet analysis

Yang

Ahuja

2013

2013 IEEE International Conference on Image Processing

View full text Add to dashboard Cite

A new rotation-invariant wavelet-based texture recognition scheme is proposed. In the previous rotation-invariant approaches, the focus is on adapting the wavelet transform or filter to rotated texture. In our approach, instead, we estimate the rotation of the texture with respect to some reference orientation, and then rotate the texture image back to the reference orientation before applying the wavelet analysis to extract features. With such rotation compensation, even very simple features (such as 1-level DWT and the subband energy) can be effective in achieving high classification accuracy as we demonstrate through our experiments.

show abstract

“…In a similar work, the authors used Gaussianized steerable pyramids for providing rotation-invariant features in [10]. Wavelet-based rotation invariance is introduced in [11] using rotated complex wavelet filters and in [12] using wavelet-based hidden Markov trees. These works show the effectiveness of their methods on the average performance.…”

Section: Introductionmentioning

confidence: 99%

Multiscale texture retrieval based on low-dimensional and rotation-invariant features of curvelet transform

Çavuşoğlu

2014

J Image Video Proc

View full text Add to dashboard Cite

Multiscale-based texture retrieval algorithms use low-dimensional feature sets in general. However, they do not have as good retrieval performances as those of the state-of-the-art techniques in the literature. The main motivation of this study is to use low-dimensional multiscale features to provide comparable retrieval performances with the state-of-the-art techniques. The proposed features of this study are low-dimensional, robust against rotation, and have better performance than the earlier multiresolution-based algorithms and the state-of-the-art techniques with low-dimensional feature sets. They are obtained through curvelet transformation and have considerably small dimensions. The rotation invariance is provided by applying a novel principal orientation alignment based on cross energies of adjacent curvelet blocks. The curvelet block pair with the highest cross energy is marked as the principle orientation, and the rest of the blocks are cycle-shifted around the principle orientation. Two separate rotation-invariant feature vectors are proposed and evaluated in this study. The first feature vector has 84 elements and contains the mean and standard deviation of curvelet blocks at each angle together with a weighting factor based on the spatial support of the curvelet coefficients. The second feature vector has 840 elements and contains the kernel density estimation (KDE) of curvelet blocks at each angle. The first and the second feature vectors are used in the classification of textures based on nearest neighbor algorithm with Euclidian and Kullback-Leibler distance measures, respectively. The proposed method is evaluated on well-known databases such as, Brodatz, TC10, TC12-t184, and TC12-horizon of Outex, UIUCTex, and KTH-TIPS. The best performance is obtained for kernel density feature vector. Mean and standard deviation feature vector also provides similar performance and has less complexity due to its smaller feature dimension. The results are reported as both precision-recall curves and classification rates and compared with the existing state-of-the-art texture retrieval techniques. It is shown through several experiments that the proposed rotation-invariant feature vectors outperform earlier multiresolution-based ones and provide comparable performances with the rest of the literature even though they have considerably small dimensions.

show abstract

Rotation-Invariant Texture Image Retrieval Using Rotated Complex Wavelet Filters

Cited by 121 publications

References 27 publications

Identification of Metallurgical Surface Finish Images-In Manufacturing Process using Fuzzy Classifier

Identification of Metallurgical Surface Finish Images-In Manufacturing Process using Fuzzy Classifier

Rotation-invariant texture recognition by rotation compensation and wavelet analysis

Multiscale texture retrieval based on low-dimensional and rotation-invariant features of curvelet transform

Contact Info

Product

Resources

About