Semantic content-based image retrieval: A comprehensive study

Alzu’bi, Ahmad; Amira, Abbes; Ramzan, Naeem

doi:10.1016/j.jvcir.2015.07.012

Cited by 185 publications

(119 citation statements)

References 251 publications

(297 reference statements)

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“…In 2015, Alzu'bi et al [35] suggested that the multimedia contents complexity is increasing dominantly in the current digital world. So this generates the need of highly effective retrieval systems.…”

Section: Related Workmentioning

confidence: 99%

Result Analysis and Comparison of Hybrid Method based on Local Binary Pattern (LBP) and Color Moment (CM) for Efficient Image Retrieval

Singh¹,

Agrawal²

2017

IJCA

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In this paper LBP and CM methods have been efficiently used for image retrieval for the content based image retrieval (CBIR) system. As LBP method may be sensible to noise in case of comparing neighboring pixels. The drawback of CM is it may be inefficient with too much details image. So it will be better to combine the feature of both method and utilized the property of both the two methods. In this paper LBP and CM are used combined along with the analysis of individual LBP and CM for the comparison. Wang database have been used for the experimentation. All the 10 classes are used for the results comparison. The results suggested that the combined method have the capability over individual methods in efficient image retrieval.

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Section: Related Workmentioning

confidence: 99%

Result Analysis and Comparison of Hybrid Method based on Local Binary Pattern (LBP) and Color Moment (CM) for Efficient Image Retrieval

Singh¹,

Agrawal²

2017

IJCA

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“…In 2015, Alzu'bi et al [31] recommended that the multimedia contents complexity in nature is expanding predominantly in the current computerized world. So this creates the need of exceptionally compelling recovery frameworks.…”

Section: Related Workmentioning

confidence: 99%

A review and analysis of content based image retrieval system

Chauhan¹

2017

TIPCV

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“…According to Alzu'bi et al (2015), precision is the ratio of the number of relevant images within the first k results to the number of total retrieved images. Recall is the ratio of the number of relevant images within the first k results to the number of total relevant images.…”

Section: Phase 2: Similar Image Retrievalmentioning

confidence: 99%

“…Image retrieval systems return a set of images which are similar to an image in a query by matching their features (Acharya and Ray, 2005;Liu et al, 2007;Liu and Yang, 2013;Muneesawang et al, 2014;Alzu'bi et al, 2015). There are two well-known techniques used in many systems: text-based and content-based (Liu et al, 2007;Acharya and Ray, 2005;Muneesawang et al, 2014;Marques and Furht, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…A content-based image retrieval system usually has two main modules: image feature extracting and indexing and an image feature similarity comparator (Liu et al, 2007;Acharya and Ray, 2005;Muneesawang et al, 2014;Alzu'bi et al, 2015). The main objectives of research on content-based image retrieval are to improve the efficiency of an image feature extractor, to reduce the size of the In this model, the features of images are computed by their colors and textures, which are segmented with the techniques of the CIEL * a * b * color space and discrete wavelet frames (DWFs).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Content-based image retrieval using a signature graph and a self-organizing map

2016

International Journal of Applied Mathematics and Computer Science

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In order to effectively retrieve a large database of images, a method of creating an image retrieval system CBIR (contentbased image retrieval) is applied based on a binary index which aims to describe features of an image object of interest. This index is called the binary signature and builds input data for the problem of matching similar images. To extract the object of interest, we propose an image segmentation method on the basis of low-level visual features including the color and texture of the image. These features are extracted at each block of the image by the discrete wavelet frame transform and the appropriate color space. On the basis of a segmented image, we create a binary signature to describe the location, color and shape of the objects of interest. In order to match similar images, we provide a similarity measure between the images based on binary signatures. Then, we present a CBIR model which combines a signature graph and a self-organizing map to cluster and store similar images. To illustrate the proposed method, experiments on image databases are reported, including COREL, Wang and MSRDI.

show abstract

Semantic content-based image retrieval: A comprehensive study

Cited by 185 publications

References 251 publications

Result Analysis and Comparison of Hybrid Method based on Local Binary Pattern (LBP) and Color Moment (CM) for Efficient Image Retrieval

Result Analysis and Comparison of Hybrid Method based on Local Binary Pattern (LBP) and Color Moment (CM) for Efficient Image Retrieval

A review and analysis of content based image retrieval system

Content-based image retrieval using a signature graph and a self-organizing map

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