An efficient texture classification algorithm using Gabor wavelet

Ahmadian, Alireza; Mostafa, Atahar

doi:10.1109/iembs.2003.1279918

Cited by 78 publications

(47 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, in Rocchio formula (Gletsos et al, 2003), the initial query is moved towards positive samples and away from negative samples by different degrees; in MEGA (Mustafa and Mostafa, 2003), positive samples are used to learn the target concept in k-CNF. While negative samples are used to learn a k-DNF that bounds the uncertain region; some researchers even come up with the idea of introducing different penalizing factors for positive and negative samples into the optimization problem of SVM.…”

Section: Relevance Feedback Methodmentioning

confidence: 99%

“…Image retrieval in general and Content-Based Image Retrieval (CBIR) is particular in the field of medical domain has been one of the most exciting and fastest growing research areas over the last decade (Nadler and Smith, 1993;Oosterveld et al, 1993;Mustafa and Mostafa, 2003). The term image retrieval means finding similar images from a large database archive with the help of some key attributes associated with the images or features inherently contained in the images.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Content Based Image Retrieval of Ultrasound Liver Diseases Based on Hybrid Approach

Suganya¹

2012

American Journal of Applied Sciences

View full text Add to dashboard Cite

Problem statement:In the past few years, immense improvement was obtained in the field of Content-Based Image Retrieval (CBIR). Nevertheless, existing systems still fail when applied to medical image databases. Simple feature-extraction algorithms that operate on the entire image for characterization of color, texture, or shape cannot be related to the descriptive semantics of medical knowledge that is extracted from images by human experts. Approach: In this study, we present a hybrid approach called Support vector machine combined with relevance feedback for the retrieval of liver diseases from Ultrasound (US) images is introduced. SVM and RF are supervised active learning technique used to improve the effectiveness of the retrieval system. Three kinds of liver diseases are identified including cyst, alcoholic cirrhosis and carcinoma. The diagnosis scheme includes four steps: image registration, feature extraction, feature selection and image retrieval. First the ultrasound images are registered in the database based on the modality. Then the features, derived from first order statistics, gray level co-occurrence matrix and fractal geometry, are obtained from the Pathology Bearing Regions (PBRs) among the normal and abnormal ultrasound images. The Correlation Based Feature Selection (CFS) algorithm selects the certain features for the specific diseases and also reduces dimensionality space for classification. Finally, we implement our hybrid approach for retrieval of specific diseases from the database. Results: This hybrid approach can get the query from user and has retrieved both positive and negative samples from the database, by getting feedback in each round from the radiologist is help to improve the retrieval of correct images. Conclusion:The hybrid approach (SVM+RF) comprises several benefits when compared to existing CBIR for medical system by neural network algorithms. Fractal geometry in feature extraction plays crucial role in ultrasound liver image retrieval. CFS also reduce the dimensionality issue during storage. Image registration plays an important role in the retrieval. It reduces the redundancy of retrieval images and increases the response rate. Getting relevance feedback from physician helps to improve the accuracy of retrieval images from the database.

show abstract

Section: Relevance Feedback Methodmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Content Based Image Retrieval of Ultrasound Liver Diseases Based on Hybrid Approach

Suganya¹

2012

American Journal of Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In structural based texture method, the surface pattern is repetitive such as floor design that contains the same pattern [11]. In statistical texture, the surface pattern is not regularly repetitive such as different flower objects in a picture that normally have similar properties but not exactly the same [12]. The common known texture descriptors are Wavelet Transform, Gabor-filter, cooccurrence matrices and Tamura features.…”

Section: Texture Featurementioning

confidence: 99%

“…Texture retrieval is a limb of texture analysis that has attracted wide attention from industries since this is well suited for the identification of products such as ceramic tiles, marble, parquet slabs, etc. Ahmadian et al have used the discrete wavelet transform (DWT) for texture classification [12]. The application of the DWT using generalized Gaussian density with KullbackLeibler distance has shown to provide efficient results for texture image retrieval [13] and image segmentation [14].…”

Section: Litrature Reviewmentioning

confidence: 99%

“…The segmented image is then the union of distinct groups, where pixels of homogeneous regions are associated to the same groups. The proposed work focus on the fuzzy cmeans algorithm (FCM, [3]), which is used by many segmentation methods [1] [13] [5] [22] [12]. After successful segmentation, boundary of the extracted object is transformed into signature whose Fast Fourier Transform is calculated.…”

Section: Segmentationmentioning

confidence: 99%

See 1 more Smart Citation

Content- based Image Retrieval Approach using Three Features Color, Texture and Shape

Jain¹,

Yadav²

2014

IJCA

View full text Add to dashboard Cite

Content Based Image Retrieval is a technique of automatic indexing and retrieving of images from a large data base. Visual features such as color, texture and shape are extracted to differentiate images in Content Based Image Retrieval (CBIR). Each of the features can be represented using one or more feature descriptors. These features descriptors combined with form feature vectors and are used together. During the retrieval, features and descriptors of the query are compared with the available images in the database. The images are then retrieved from database on the basis of distance of their feature vectors. At present, information of the maximum two features have been utilized for comparing the image and these methods provides the less accurate result. In our proposed work, more than two features i.e. three features are used for comparison and retrieval of image from the database. These three features are color, shape & texture features for image retrieval and provide more accurate results. These features are combined to fulfil the aspect of retrieval in image. The proposed work uses HSI color information especially Hue value, Fuzzy C-Mean algorithm for shape representation and co-occurrence matrix is used for texture feature extraction.

show abstract

Leaf Recognition Based on the Combination of Wavelet Transform and Gaussian Interpolation

Wang

2005

Lecture Notes in Computer Science

View full text Add to dashboard Cite

An efficient texture classification algorithm using Gabor wavelet

Cited by 78 publications

References 5 publications

Content Based Image Retrieval of Ultrasound Liver Diseases Based on Hybrid Approach

Content Based Image Retrieval of Ultrasound Liver Diseases Based on Hybrid Approach

Content- based Image Retrieval Approach using Three Features Color, Texture and Shape

Leaf Recognition Based on the Combination of Wavelet Transform and Gaussian Interpolation

Contact Info

Product

Resources

About