CBIR using Texels of colour Fuzzy Textons

Putheti, Sudhakar; Edara, Srinivasa Reddy; Edara, Sai Alekya

doi:10.1109/his.2012.6421378

Cited by 4 publications

(15 citation statements)

References 10 publications

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“…As per Julesz [17] description a texton is a pattern which is shared by an image as a common property. Textures will be formed only if the adjacent elements lie within the neighborhood.…”

Section: Rough Textonmentioning

confidence: 99%

“…Texton Image has the discrimination power of color, texture and shape features. Based on the Julez's [17] textons theory the texton co-occurrence matrices (TCM) algorithm, can describe the spatial correlation of textons for image retrieval. With the use of limited number of selected pixels the algorithm computes different features.…”

Section: Rough Textonmentioning

confidence: 99%

“…The computational complexity for using the overlapped components of 12 textons is also less to obtain final texton image. The 12 textons of 3 X 3 grid [17] is used in the proposed method.…”

Section: Rough Textonmentioning

confidence: 99%

“…Determination of texture property can be done based on the values of Texture Unit and Texture Unit Number of an image plane [17]. A 3x3 pixel square will be constructed to define a basic unit of the method.…”

Section: Figure 2 12 Types Of 3x3 Textonsmentioning

confidence: 99%

“…Texture Unit (TU) is nothing but the unit which best suits for the local texture feature of a given pixel and its neighborhoods, in all directions of a square raster. Texture Unit (TU) as in [17], will be given by TU={E1, E2, …,E8}, where:…”

Section: Figure 2 12 Types Of 3x3 Textonsmentioning

confidence: 99%

See 4 more Smart Citations

Rough Texton based Fundus Image Retrieval

Sadarajupalli¹,

Putheti²

2015

IJCA

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Content based image retrieval (CBIR), is a robust technique widely used in the field of image retrieval. This method uses visual contents like color, texture and shape, to search images from a large scale database of images. Among the primary image contents, texture is an important spatial feature. Texton is a statistical approach used to analyze the texture of an image. Texture-based approach proposed here can take into account the vagueness of images also while retrieving images just as an expert manually retrieves medical images.

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“…As per Julesz [17] description a texton is a pattern which is shared by an image as a common property. Textures will be formed only if the adjacent elements lie within the neighborhood.…”

Section: Rough Textonmentioning

confidence: 99%

Section: Rough Textonmentioning

confidence: 99%

“…The computational complexity for using the overlapped components of 12 textons is also less to obtain final texton image. The 12 textons of 3 X 3 grid [17] is used in the proposed method.…”

Section: Rough Textonmentioning

confidence: 99%

Section: Figure 2 12 Types Of 3x3 Textonsmentioning

confidence: 99%

Section: Figure 2 12 Types Of 3x3 Textonsmentioning

confidence: 99%

See 3 more Smart Citations

Rough Texton based Fundus Image Retrieval

Sadarajupalli¹,

Putheti²

2015

IJCA

Self Cite

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Content Based Image Retrieval Using Fuzzy Texton and Shearlet Transform

Putheti

Prasad

Edara

2015

Lecture Notes in Electrical Engineering

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Abstract. This paper presents a novel and an efficient content based image retrieval technique based on content color, texture and shape, which are primary low level features to describe image. The proposed method uses fuzzy texton method to extract texture features and discrete shearlet transform for shape features in addition to color features. Fuzzy Texton is a way to describe the texture properties in image analysis. Shearlet transforms can analyze signals defined not only at various orientations but also at multiple scales. The test results emerged more efficient than all the predecessor peer techniques like Texton Co-occurrence Matrix (TCM), Texton Multi Histogram (TMH) and Texels of Fuzzy Texton (TT).

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Classification of Normal, Benign and Malignant Tissues using Fuzzy Texton and Support Vector Machine in Mammographic Images

RaghaDeepthiLoka¹,

Putheti²

2013

IJCA

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Content Based Image Retrieval systems are helpful to the radiologists in diagnosis of breast cancer. This paper presents a method for retrieving breast tissue as normal, benign or malignant in mammograms by using FuzzyTextons. In feature extraction first fuzzy texton images of mammograms are calculated. During the detection of fuzzy texton, fuzzy based quantization is performed to get more accurate textons. Then feature vectors are extracted for fuzzy textons and for efficient classification and retrieval Support Vector Machine is used. The proposed method was tested for a mammogram set from MIAS database.

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CBIR using Texels of colour Fuzzy Textons

Cited by 4 publications

References 10 publications

Rough Texton based Fundus Image Retrieval

Rough Texton based Fundus Image Retrieval

Content Based Image Retrieval Using Fuzzy Texton and Shearlet Transform

Classification of Normal, Benign and Malignant Tissues using Fuzzy Texton and Support Vector Machine in Mammographic Images

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