Image indexing using moments and wavelets

Mandal, Mrinal; Aboulnasr, T.; Panchanathan, S.

doi:10.1109/30.536156

Cited by 147 publications

(56 citation statements)

References 13 publications

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“…We also plan to move beyond character based metrics to software metrics that are computed by parsing the source code. We also would like to consider other classification mechanisms such as histogram moments [6]. Lastly, we wish to be able to determine if multiple developers have authored a single piece of source code, and automatically partition the code based on authorship.…”

Section: Future Workmentioning

confidence: 99%

A Probabilistic Approach to Source Code Authorship Identification

Kothari

Shevertalov

Stehle

et al. 2007

Fourth International Conference on Information Technology (ITNG'07)

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Section: Future Workmentioning

confidence: 99%

A Probabilistic Approach to Source Code Authorship Identification

Kothari

Shevertalov

Stehle

et al. 2007

Fourth International Conference on Information Technology (ITNG'07)

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“…It has been mathematically shown that SVD-based approach provides the lower bounds on the histogram distance measure. Mandal et al [10] reduce the computational complexity of color histogram comparison by representing the histogram in terms of its moments. Experimental results also indicate that Legendre moments provide superior retrieval performance compared to regular moments [10].…”

Section: B Dimensionalitymentioning

confidence: 99%

“…For example, given an ordered color histogram with eight bins and , where , the membership matrix of cumulative color histogram is (10) Note that inherently cumulative color histogram also considers the color similarity across all color bins. However, FCH is more general as its fuzzy (rather than crisp) membership matrix can be adjusted according to different noise interference and applications.…”

Section: Relationship Between Fch and Other Color Histogramsmentioning

confidence: 99%

Fuzzy color histogram and its use in color image retrieval

Han

2002

IEEE Trans. on Image Process.

243

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“…As Wavelets can be combined, using a "shift, multiply and sum" technique called convolution, with portions of an unknown signal to extract information from the unknown signal. They have advantages over traditional fourier methods in analyzing physical situations where the signal contains discontinuities and sharp spikes [10], [22], [23], [28]. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Images Using DCT and DCT Wavelet Over Image Blocks

Kekre¹,

Sonawane²

2011

IJACSA

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Abstract-This paper introduces a new CBIR system based on two different approaches in order to achieve the retrieval efficiency and accuracy. Color and texture information is extracted and used in this work to form the feature vector. To do the texture feature extraction this system uses DCT and DCT Wavelet transform to generate the feature vectors of the query and database images. Color information extraction process includes separation of image into R, G and B planes. Further each plane is divided into 4 blocks and for each block row mean vectors are calculated. DCT and DCT wavelet is applied over row mean vector of each block separately and 4 sets of DCT and DCT wavelet coefficients are obtained respectively. Out of these few coefficients are selected from each block and arranged in consecutive order to form the feature vector of the image. Variable size feature vectors are formed by changing the no of coefficients selected from each row vector. Total 18 different sets are obtained by changing the no of coefficients selected from each block. These two different feature databases obtained using DCT and DCT wavelet are then tested using 100 query images from 10 different categories. Euclidean distance is used as similarity measure to compare the image features. Euclidean distance calculated is sorted into ascending order and cluster of first 100 images is selected to count the images which are relevant to the query image. Results are further refined using second level thresholding which uses three criteria which can be applied to first level results. Results obtained are showing the better performance by DCT wavelet as compare to DCT transform.

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Image indexing using moments and wavelets

Cited by 147 publications

References 13 publications

A Probabilistic Approach to Source Code Authorship Identification

A Probabilistic Approach to Source Code Authorship Identification

Fuzzy color histogram and its use in color image retrieval

Retrieval of Images Using DCT and DCT Wavelet Over Image Blocks

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