Rotationally invariant texture based features (original) (raw)

Texture features based on local Fourier histogram: self-compensation against rotation

Journal of Electronic Imaging, 2008

We present a method of introducing rotation invariance in texture features based on a local Fourier histogram (LFH) computed using a 1-D discrete Fourier transform (DFT). To compensate for image rotation, a local image-gradient angle at each image pixel is found from within one of the 1-D DFT coefficients. The rotation invariance is established theoretically, analytically as well as empirically. The rotation-compensated features extracted from the same texture image oriented at different angles exhibit very high cross correlation. Therefore, the proposed texture features are expected to yield very high accuracies for a variety of image data and applications. The improved LFH-based features outperform the earlier version of the features and the features based on Gabor filters in texture recognition on 8560 images from the Brodatz album. © 2008 SPIE and IS&T. Downloaded from SPIE Digital Library on 29 Nov 2009 to 58.181.107.36. Terms of Use: http://spiedl.org/terms

ROTATIONALLY INVARIANT TEXTURE CLASSIFICATION

Texture based features used for content based retrieval of images and videos should ideally be invariant to simple transforms such as rotation. This paper introduces the recently developed dual tree complex wavelet transform (DT-CWT) as a tool to extract rotationally invariant texture based features. When applied in two dimensions the DT-CWT produces shift invariant and orientated subbands at each decomposition scale. Rotationally invariant features can be extracted from the energies of these subbands whilst benefiting from the computational efficiency of the decomposition and the ability to choose the transform filters.

One-dimensional Fourier transform coefficients for rotation invariant texture classification

Machine Vision Applications, Architectures, and Systems Integration V, 1996

This paper introduces a texture descriptor that is invariant to rotation. The new texture descriptor utilizes the property of the magnitudes of Fourier transform coefficients that do not change with spatial shift of input elements. Since rotating an image by an arbitrary angle does not change pixel intensities in an image but shift them in circular motion, the notion of producing textural features invariant to rotation using 1D Fourier transform coefficients can be realized if the relationship between circular motion and spatial shift can be established. By analyzing pixels in a circular neighborhood in an image, a number of FOurier transform coefficients can be generated to describe local properties of the neighborhood. From the magnitudes of these coefficients, several rotation invariant features are obtained to represent each texture class. Based on these features, an unknown image is assigned to one of the known classes using a nearest neighbor classifier. All of the feature samples for the classifier are extracted from unrotated texture images only. The new texture descriptor outperformed the circular simultaneous autoregressive model in classifying rotated texture images taken from 30 texture classes. ©2005 Copyright SPIE - The International Society for Optical Engineering. http://spie.org/Publications/Proceedings/Paper/10.1117/12.257258 http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1025128

Improved Texture Descriptor with Features based on Fourier Transform

This paper presents an improved version of the features based on Discrete Fourier Transform (DFT) for texture description that demonstrate robustness against rotation. The features have been tested on cropped parts of textures from Brodatz collection and their rotated versions. The results show improved performance for both, recognition as well as retrieval as compared to texture features based on Gabor filter and older version of the DFT based features.

Texture feature extraction in the spatial-frequency domain for content-based image retrieval

2010

The advent of large scale multimedia databases has led to great challenges in content-based image retrieval (CBIR). Even though CBIR is considered an emerging field of research, however it constitutes a strong background for new methodologies and systems implementations. Therefore, many research contributions are focusing on techniques enabling higher image retrieval accuracy while preserving low level of computational complexity. Image retrieval based on texture features is receiving special attention because of the omnipresence of this visual feature in most real-world images. This paper highlights the state-of-the-art and current progress relevant to texture-based image retrieval and spatial-frequency image representations. In particular, it gives an overview of statistical methodologies and techniques employed for texture feature extraction using most popular spatial-frequency image transforms, namely discrete wavelets, Gabor wavelets, dual-tree complex wavelet and contourlets. Indications are also given about used similarity measurement functions and most important achieved results.

Improved Texture Description with Features Based on Fourier Transform

… Processing and Systems, 2009

Rotation Invariant Content-Based Image Retrieval System

2014

The emergence of multimedia technology and the rapid growth in the number and type of multimedia assets controlled by several entities, yet because the increasing range of image and video documents showing on the Internet, have attracted vital analysis efforts in providing tools for effective retrieval and management of visual data. So the need for image retrieval system arose. Out of many existing systems “ROTATION INVARIANT CONTENT-BASED IMAGE RETRIEVAL SYSTEM” is the most efficient and accurate one. Effective texture feature is an essential component in any CBIR system. In the past, spectral features like Gabor and Wavelet have shown superior retrieval performance than most statistical and structural options. Recent researches on multi-resolution analysis have found that curvelet captures texture properties like curves, lines and edges with additional accuracy than Gabor filters. However, the texture feature extracted using curvelet transform is not rotation invariant. This can d...

Texture Retrieval with Descriptors Based on Local Fourier Transform: Comparing the Rectangular and Circular Neighbourhoods

muet.edu.pk

The texture descriptors derived from 1-D DFT (Discrete Fourier Transform) of the pixel values of a local neighbourhood have been shown to perform better than the methods based on wavelets for image retrieval and recognition. These DFT-based texture descriptors were extracted from rectangular or circular neighbourhoods. This paper compares the texture descriptors extracted from rectangular and the circular neighbourhoods previously proposed in the literature. A database of images is constructed from Brodatz album and the texture descriptors extracted from the two types of neighbourhoods are compared for texture retrieval. This paper shows that extracting DFT-based features from circular neighbourhood is almost thrice as expensive as extracting the same from the rectangular neighbourhood. The results of image retrieval on a large image database show that the descriptor extracted from rectangular neighbourhoods performs better than the same extracted from the circular neighbourhoods.

Rotation-Invariant Features for Texture Image Classification

2006

Texture features based on wavelet transform are the direction of eigenvector corresponding to maximum sensitive to texture rotation and translation. This paper develops eigenvalue. Once its angle is known, a negative rotation is a new rotation invariant texture analysis technique using given to align the texture to a particular orientation for any Principal Components Analysis (PCA) and wavelet transform. 0. to 1800. Then the wavelet transform

Rotationally invariant texture based features (original) (raw)

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