A Novel Approach for Content Based Image Retrieval

Singh, Nidhi; Singh, Kanchan; Sinha, A. K.

doi:10.1016/j.protcy.2012.05.037

Cited by 24 publications

(13 citation statements)

References 17 publications

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“…Analyzing images in real-time dynamic environment is not feasible. A system is proposed in [18] that analyze images in real-time environment. It extracts shape and color to represents image contents.…”

Section: Related Workmentioning

confidence: 99%

Content-Based Image Retrieval Using Texture Color Shape and Region

Shirazi¹,

Umar²,

Naz³

et al. 2016

ijacsa

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Abstract-Interests to accurately retrieve required images from databases of digital images are growing day by day. Images are represented by certain features to facilitate accurate retrieval of the required images. These features include Texture, Color, Shape and Region. It is a hot research area and researchers have developed many techniques to use these feature for accurate retrieval of required images from the databases. In this paper we present a literature survey of the Content Based Image Retrieval (CBIR) techniques based on Texture, Color, Shape and Region. We also review some of the state of the art tools developed for CBIR.

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Section: Related Workmentioning

confidence: 99%

Content-Based Image Retrieval Using Texture Color Shape and Region

Shirazi¹,

Umar²,

Naz³

et al. 2016

ijacsa

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“…[5] developed a novel approach in which the first feature vector is obtained by applying Fast Fourier transform on the signature obtained by image segmentation using Fuzzy C-mean algorithm. Hue and saturation component constitute the second feature vector.…”

Section: Related Workmentioning

confidence: 99%

“…yg;j gives the probability that a pixel at distance k away from the given pixel Ci is of color C j The autocorrelogram of I is defined as (5) It captures the spatial correlation between identical colors only. The points which show a tendency for clustering or for anti-clustering are easily discriminated using the color autocorrelogram.…”

Section: Prmentioning

confidence: 99%

Quick interactive image search in huge databases using Content-Based image retrieval

Hiwale¹,

Dhotre²,

Bamnote³

2015

2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)

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The number of digital images over the web is growing by each passing day, indexing this image data based on text is tiresome and error prone. Content-Based image retrieval uses the computer vision techniques to efficiently solve the image retrieval problem. In this research paper we propose a CBrn system which extracts the features of digital image to retrieve similar images from huge databases. We have used Color Histogram, Color Auto-Correlogram, Color Moment, Gabor Wavelet and Discrete Wavelet transform to extract image features. The images are classified using Support Vector Machine (SVM) classifier which effectively distinguishes between relevant and irrelevant images. The results depict that proposed method has better precision and recall rate compared to other methods.The proposed method has average normal precision rate of 0.76 and average normal recall rate of 0.69.

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“…There are many methods and approaches proposed for Content based Image retrieval till date. The most popular methods yields results on visual contents of the image, such as color, texture and shape [2].The distance measures are used to match the query feature with the dataset feature. The Euclidean distance measure is popular among other and widely used for the similarity measure.…”

Section: Introductionmentioning

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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A Novel Approach for Content Based Image Retrieval

Cited by 24 publications

References 17 publications

Content-Based Image Retrieval Using Texture Color Shape and Region

Content-Based Image Retrieval Using Texture Color Shape and Region

Quick interactive image search in huge databases using Content-Based image retrieval

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

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