An integrated color and texture feature based framework for content based image retrieval using 2D Wavelet Transform

Balamurugan, V.; Anandhakumar, P.

doi:10.1109/icccnet.2008.4787734

Cited by 14 publications

(12 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main reason behind using wavelet transform in a feature extraction task is that it is computationally cheap and the resulted 40 feature vectors are from a low dimensionality while they are discriminant enough. Moreover, it has been successfully applied in Content Based Image Retrieval (CBIR) and image classification scenarios with high performance [18][19][20][21][22]. Experimental results show that our improvements to the Three-phased PSO-OSD enhance the RBFNN's classification accuracy to a high degree.…”

Section: Introductionmentioning

confidence: 88%

An improved radial basis function neural network for object image retrieval

Montazer

Giveki

2015

Neurocomputing

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 88%

An improved radial basis function neural network for object image retrieval

Montazer

Giveki

2015

Neurocomputing

View full text Add to dashboard Cite

“…Diabetes has become one of the rapidly increasing health threats worldwide [21]. Only in Finland, there are 30 000 people diagnosed to the type 1 maturity onset diabetes in the young, and 200 000 people diagnosed to the type 2 latent autoimmune diabetes in adults [4]. In addition, the current estimate predicts that there are 50 000 undiagnosed patients [4].…”

Section: Introductionmentioning

confidence: 95%

“…Existing general-purpose CBIR systems roughly fall into three categories depending on the approach to extract signatures: histogram, color layout, and region-based search. There are also systems that combine retrieval results from individual algorithms by a weighted sum matching metric [4], or other merging schemes [25]. After extracting signatures, the next step is to determine a comparison rule, including a querying scheme and the definition of a similarity measure between images.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Only in Finland, there are 30 000 people diagnosed to the type 1 maturity onset diabetes in the young, and 200 000 people diagnosed to the type 2 latent autoimmune diabetes in adults [4]. In addition, the current estimate predicts that there are 50 000 undiagnosed patients [4]. Proper and early treatment of diabetes is cost effective since the implications of poor or late treatment are very expensive.…”

Section: Introductionmentioning

confidence: 96%

See 1 more Smart Citation

Early Detection of High Blood Pressure and Diabetic Retinopathy on Retinal Fundus Images Using CBRIR Based on Lifting Wavelets

Tadasare

Pawar

2018

IJAAS

View full text Add to dashboard Cite

In this paper we present a lifting wavelet based CBRIR image retrieval system that uses color and texture as visual features to describe the content of a retinal fundus images. Our contribution is of three directions. First, we use lifting wavelets 9/7 for lossy and SPL5/3 for lossless to extract texture features from arbitrary shaped retinal fundus regions separated from an image to increase the system effectiveness. This process is performed offline before query processing, therefore to answer a query our system does not need to search the entire database images; instead just a number of similar class type patient images are required to be searched for image similarity. Third, to further increase the retrieval accuracy of our system, we combine the region based features extracted from image regions, with global features extracted from the whole image, which are texture using lifting wavelet and HSV color histograms. Our proposed system has the advantage of increasing the retrieval accuracy and decreasing the retrieval time. The experimental evaluation of the system is based on a db1 online retinal fundus color image database. From the experimental results, it is evident that our system performs significantly better accuracy as compared with traditional wavelet based systems. In our simulation analysis, we provide a comparison between retrieval results based on features extracted from the whole image using lossless 5/3 lifting wavelet and features extracted using lossless 9/7 lifting wavelet and using traditional wavelet. The results demonstrate that each type of feature is effective for a particular type of disease of retinal fundus images according to its semantic contents, and using lossless 5/3 lifting wavelet of them gives better retrieval results for almost all semantic classes and outperform 4-10% more accuracy than traditional wavelet.

show abstract

“…Wavelet transform have been used most widely in many aspects of image processing such as noise removal, image compression, image super resolution and image retrieval. The texture feature of an image is extracted by mean and variance of the wavelet subbands .But wavelets [2][3][4] loses their universality in capturing the edge discontinuities in image which is important in texture representation.…”

Section: Introductionmentioning

confidence: 99%