An Implementation of Feature Extraction over Medical Images on Open Cv Environment

Kaur, Bhavneet; Jindal, Sonika

doi:10.1109/icdccom.2014.7024695

Cited by 8 publications

(7 citation statements)

References 12 publications

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“…Bhavneet Kaur et al [27] used the OPEN CV platform since it provides a C interface to implement various image processing algorithms. The work merges the feature extraction technique with this most suitable platform available for image algorithms.…”

Section: Related Workmentioning

confidence: 99%

Features Extraction using Local Binary Patterns and Steerable Pyramids for Efficient Image Retrieval

Rani¹,

Jindal²

2018

IJCA

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This present a hybrid approach of image classification using KNN and feature extraction using LBP and steerable pyramid based image retrieval system that uses color, contours and texture as visual features to describe the content of an image. In this k-nearest neighbor image classification mechanism is used to fetch the appropriate images from the database image set using the query image and the database images are reduced to images returned after classification mechanism which leads to decrease in the number of irrelevant images. Steerable pyramid applied to extract features from query image and candidate images retrieved from the KNN and store them in feature features. Local Binary Pattern (LBP) is one of the techniques used in image classification and has been used for extracting the shape of the images. The experimental evaluation of the system is based on a Wang data set. Various parameters like precision, recall, computation time and matching time have been computed to analyze the results that are recorded iteratively for different images as input. From the experimental results, it is evident that proposed system performs significantly better and faster compared with other existing systems. The results demonstrate that each type of feature is effective for a particular type of images according to its semantic contents, and using a combination of them giving better retrieval results for almost all different classes of images in the data set.

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Section: Related Workmentioning

confidence: 99%

Features Extraction using Local Binary Patterns and Steerable Pyramids for Efficient Image Retrieval

Rani¹,

Jindal²

2018

IJCA

Self Cite

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“…SIFT [9] The precision of the system was 80% for MRI, 75% for Iris and 90% for Bones. SURF [10] Dynamic delimitation is more effective and robust than static delimitation.…”

Section: Article Evaluationmentioning

confidence: 99%

A CBIR System for the Recognition of Agricultural Machinery

Ruiz¹,

Rodríguez-Mazahua²,

López-Chau³

et al. 2018

RCS

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Content-Based Image Retrieval (CBIR) is a technique to obtain images based on visual characteristics like color, texture and shape. In this article, an analysis of different CBIR systems is presented; also an inventory system that allows content-based queries is proposed to help the employees of a company that sells agricultural machinery and spare parts, called AGROMAQ. This system identifies different products, all with the aim of reducing customer service time, since an expert is currently needed to identify them, which increases attention time not only in hours but also in days.

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“…Compared with colour features, texture features indicate the macrostructure and microstructure of the images. [16] …”

Section: Image Retrieval Using Texture Contentmentioning

confidence: 99%

“…SVM is best tool for regression and classification [16]. Support vector machine may be defined as this is the linear function of the high dimensionality feature space which consist of the postulate space.…”

Section: Support Vector Machinementioning

confidence: 99%

Content based Image Retrieval using Selective Region Matching with Region of Interest and SVM

Charaya¹,

Jindal²,

Kaur³

2016

IJCA

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Recently, very large collections of images and videos have grown rapidly. In parallel with this growth, content-based retrieval and querying the indexed collections are required to access visual information. Three main components of the visual information are color, texture and shape. In this paper, a selective region based content-based image retrieval system is presented that combines two visual descriptors of images and measures similarity of images by applying a SVM Classification. Paper. Here, the selective region matching with Region of Interest and SVM classification based CBIR retrieval system for imaging is presented in order to provide better image classification and fast image retrieval. In the proposed method the color and texture features like contrast, mean, standard deviation, energy and entropy are extracted from the image. Also it is shown through experimental results and analysis of retrieval effectiveness of querying that the content-based retrieval system is effective in terms of retrieval and scalability.

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An Implementation of Feature Extraction over Medical Images on Open Cv Environment

Cited by 8 publications

References 12 publications

Features Extraction using Local Binary Patterns and Steerable Pyramids for Efficient Image Retrieval

Features Extraction using Local Binary Patterns and Steerable Pyramids for Efficient Image Retrieval

A CBIR System for the Recognition of Agricultural Machinery

Content based Image Retrieval using Selective Region Matching with Region of Interest and SVM

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