RETRACTED: Invariance image analysis using modified Zernike moments

EURASIP J. Adv. Signal Process.

2013

Audio identification via fingerprint has been an active research field for years. However, most previously reported methods work on the raw audio format in spite of the fact that nowadays compressed format audio, especially MP3 music, has grown into the dominant way to store music on personal computers and/or transmit it over the Internet. It will be interesting if a compressed unknown audio fragment could be directly recognized from the database without decompressing it into the wave format at first. So far, very few algorithms run directly on the compressed domain for music information retrieval, and most of them take advantage of the modified discrete cosine transform coefficients or derived cepstrum and energy type of features. As a first attempt, we propose in this paper utilizing compressed domain auditory Zernike moment adapted from image processing techniques as the key feature to devise a novel robust audio identification algorithm. Such fingerprint exhibits strong robustness, due to its statistically stable nature, against various audio signal distortions such as recompression, noise contamination, echo adding, equalization, band-pass filtering, pitch shifting, and slight time scale modification. Experimental results show that in a music database which is composed of 21,185 MP3 songs, a 10-s long music segment is able to identify its original near-duplicate recording, with average top-5 hit rate up to 90% or above even under severe audio signal distortions.

Section: Effect Of Moment Ordersmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Low-order auditory Zernike moment: a novel approach for robust music identification in the compressed domain

Xiao

EURASIP J. Adv. Signal Process.

2013

“…In the several past years, researchers in image analysis often used the orthogonal invariance moment [2][3][4][5] , especially the Zernike moment [6][7][8][9][10] , pertaining to region-based descriptor to represent the image shape feature [4] , because they rely not only on the contour pixels but also on all pixels constituting the shape [11,12] . Zernike moment has many invariant characteristics, including translation invariance, rotation invariance and scale invariance [10][11][12][13][14] .…”

Section: Introductionmentioning

confidence: 99%

Evolutionary computation based optimization of image Zernike moments shape feature vector

Wuhan Univ. J. Nat. Sci.

Ming

et al. 2008

The image shape feature can be described by the image Zernike moments. In this paper, we points out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the original image but has too many elements making trouble for the next image analysis phases. Then the low dimension image Zernike moments shape feature vector should be improved and optimized to describe more detail of the original image. So the optimization algorithm based on evolutionary computation is designed and implemented in this paper to solve this problem. The experimental results demonstrate the feasibility of the optimization algorithm.

“…The Zernike moment, one kind of the orthogonal invariance moments, is the most commonly used technique in image shape feature extraction and description. Many researchers have paid much more attention to its invariant characteristics, including translation invariance, rotation invariance and scale invariance [3,[6][7][8][9][10] .…”

Section: Introductionmentioning

confidence: 99%

Image Zernike moments shape feature evaluation based on image reconstruction

Geo-spatial Information Science

He²,

Yao³

2007

The evaluation approach to the accuracy of the image feature descriptors plays an important role in image feature extraction. We point out that the image shape feature can be described by the Zernike moments set while briefly introducing the basic concept of the Zernike moment. After talking about the image reconstruction technique based on the inverse transformation of Zernike moment, the evaluation approach to the accuracy of the Zernike moments shape feature via the dissimilarity degree and the reconstruction ratio between the original image and the reconstructed image is proposed. The experiment results demonstrate the feasibility of this evaluation approach to image Zernike moments shape feature.