Image retrieval using edge based shape similarity with multiresolution enhanced orthogonal polynomials model

Krishnamoorthy, Rajagopal; Devi, S. Sathiya

doi:10.1016/j.dsp.2012.09.018

Cited by 26 publications

(8 citation statements)

References 26 publications

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“…This metric is known as the Manhattan distance. The city block calculates the robustness to outliers where this distance metric is calculated by the sum of absolute between two feature vectors of images [10,11].…”

Section: City Block Distancementioning

confidence: 99%

Re-Ranking Images Retrieval: Combined Multiple Features Method

P*¹,

JAYAKUMAR²

2019

IJRTE

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Content-Based Image Retrieval (CBIR) grown rapidly in multimedia field, image retrieval, pattern recognition, etc. CBIR provides an effective way of image search and retrieval from the pool image databases. Learning effective relevance measures plays a critical role in improving the performance of image retrieval systems. In this paper present a Combined multiple features method which is two key parameters (i) Feature extraction, (ii) Similarity metrics for content-based image retrieval method. Feature extraction and similarity metrics important role in Content-Based Image Retrieval. We define hybrid feature extraction and similarity method for finding the most similar images retrieved. Combined features extraction using the various image features. These papers explain some important distance metrics such as Euclidean distance and City block distance. The experiments are performed using the various kinds of databases such as WANG Database, Corel Dataset. The experimental result shows that the proposed method is proved more effective than existing methods.

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Section: City Block Distancementioning

confidence: 99%

Re-Ranking Images Retrieval: Combined Multiple Features Method

P*¹,

JAYAKUMAR²

2019

IJRTE

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“…It aims is retrieve relevant based on the semantic and visual content of image. A number of research done in the area of CBIR based on shape [1][2], color [3] and texture [4]. First histogram method is proposed by Swain et al [5] was based on histogram intersection between the query image and database image.…”

Section: Introductionmentioning

confidence: 99%

Content Based Image Retrieval using First Order Derivates and Wavelet Moments

Kothyari¹,

Dwivedi²,

Mandoria³

2016

IJERT

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Content Based Image Retrieval uses the visual content of a picture such as color, shape, texture and indexes the image. In this paper a method is proposed for content based image retrieval. The proposed method based on a combination of statistical feature of color histogram of RGB image and wavelet moments. In this method histogram divides in some number of bins and for every bin we compute the statistical value and these values are combined with the wavelet moments to maintain a feature vector. Finally we get top image by comparing the query image with the database image on the basis of feature vector by using Euclidean distance measure.

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“…Several CBIR Smeulders et al (2000); Vailaya et al (2001) schemes were developed for indexing and retrieving images from database based on the significant features like colour Shrivastava and Tyagi (2014), texture Shi et al (2007) and shape Krishnamoorthy and Devi (2013); Chahooki and Charkari (2012). The straightforward image to image searching mechanism is not considered in CBIR.…”

Section: Introductionmentioning

confidence: 99%

Content-based image retrieval using SVD-based Eigen images

Varish

Pal

2016

IJIM

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In this paper, a content-based image retrieval scheme using the singular value decomposition (SVD) is proposed where the feature vector was estimated from the selected significant components of a singular value decomposed image. The Eigen values of the decomposed image are divided into several numbers of groups and from each group we have constructed several Eigen images and subsequently, statistical values like mean, standard deviation and entropy are computed from those Eigen images. The constructed Eigen images are suitable to analyse the original image data in various image planes. This approach is applied to each colour components for formation of colourbased final feature vector. This approach is appropriate to reduce the overall processing cost in image retrieving process due to the consideration of significant image feature in SVD domain. The scheme is tested on a standard Corel image database and satisfactoryresults are achieved.

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Image retrieval using edge based shape similarity with multiresolution enhanced orthogonal polynomials model

Cited by 26 publications

References 26 publications

Re-Ranking Images Retrieval: Combined Multiple Features Method

Re-Ranking Images Retrieval: Combined Multiple Features Method

Content Based Image Retrieval using First Order Derivates and Wavelet Moments

Content-based image retrieval using SVD-based Eigen images

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