A New Clustering Algorithm for Noisy Image Retrieval

Analoui, Morteza; Beheshti, Maedeh

doi:10.1007/978-1-4614-1695-1_22

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Effect Of Noise1

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“…1) In salt and pepper noise it takes either minimum or maximum values in dynamic range due to this reason it is also called as "Impulsive " noise. For efficient retrieval of images are on the noisy images [9]. 2) Gaussian noise is statistical noise which has its probability density function equals to normal distribution also known as the Gaussian distribution.…”

Section: Effect Of Noisementioning

confidence: 99%

Content based Image Retrieval using Median Binary Pattern with Structure Element Descriptor on Noisy Images

Desai¹

2019

IJRASET

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Effective features are essential issue for the representation and recognition of images. In this paper, we present a new feature representation method for image retrieval based on Median Binary Pattern (MBP) and Structure Element Descriptor, named Median Binary Pattern extended structure element descriptor (MBP-SED). Firstly, the gray scale image is converted using the MBP. The elements in the structure can effectively represent the bitmap which is generated by MBP. Global Feature is achieved using the combination of MBP-SED and color Histogram (CH). Proposed system is more reliable on noisy images. Experiments on Corel-1k dataset show that MBP-SED outperforms than other descriptors.

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Section: Effect Of Noisementioning

confidence: 99%