Content Based Image Retrieval by Using Color Descriptor and Discrete Wavelet Transform

Ashraf, Rehan; Ahmed, Mudassar; Jabbar, Sohail; Khalid, Shehzad; Ahmad, Awais; Din, Sadia; Jeon, Gwangil

doi:10.1007/s10916-017-0880-7

Cited by 98 publications

(54 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ashraf et al [55] presented a CBIR model that is based on color and discrete wavelet transform (DWT). For the retrieval of similar images, the low-level feature color, texture, and shape are used.…”

Section: Low-level Feature Fusionmentioning

confidence: 99%

“…Different types of features and feature extraction technique are discussed and scenarios in which feature extraction technique is good are explained [55]. To prepare the eigenvector information from the image [55], color edge detection and discrete wavelet approaches are used. e color space RGB and YCbCr are used to extract the color features.…”

Section: Low-level Feature Fusionmentioning

confidence: 99%

“…e color space RGB and YCbCr are used to extract the color features. e researchers in [55] transformed RGB images to YCbCr color space to extract the meaningful information. e YCbCr transformation is selected in this case because the human visual system can view different colors and brightness sensitivity.…”

Section: Low-level Feature Fusionmentioning

confidence: 99%

See 2 more Smart Citations

Content‐Based Image Retrieval and Feature Extraction: A Comprehensive Review

Latif

Rasheed

Sajid

et al. 2019

Mathematical Problems in Engineering

254

View full text Add to dashboard Cite

Multimedia content analysis is applied in different real-world computer vision applications, and digital images constitute a major part of multimedia data. In last few years, the complexity of multimedia contents, especially the images, has grown exponentially, and on daily basis, more than millions of images are uploaded at different archives such as Twitter, Facebook, and Instagram. To search for a relevant image from an archive is a challenging research problem for computer vision research community. Most of the search engines retrieve images on the basis of traditional text-based approaches that rely on captions and metadata. In the last two decades, extensive research is reported for content-based image retrieval (CBIR), image classification, and analysis. In CBIR and image classification-based models, high-level image visuals are represented in the form of feature vectors that consists of numerical values. The research shows that there is a significant gap between image feature representation and human visual understanding. Due to this reason, the research presented in this area is focused to reduce the semantic gap between the image feature representation and human visual understanding. In this paper, we aim to present a comprehensive review of the recent development in the area of CBIR and image representation. We analyzed the main aspects of various image retrieval and image representation models from low-level feature extraction to recent semantic deep-learning approaches. The important concepts and major research studies based on CBIR and image representation are discussed in detail, and future research directions are concluded to inspire further research in this area.

show abstract

Section: Low-level Feature Fusionmentioning

confidence: 99%

Section: Low-level Feature Fusionmentioning

confidence: 99%

See 1 more Smart Citation

Content‐Based Image Retrieval and Feature Extraction: A Comprehensive Review

Latif

Rasheed

Sajid

et al. 2019

Mathematical Problems in Engineering

254

View full text Add to dashboard Cite

show abstract

“…This section presents a brief description of some recent work in the CBIR field that has used a combination of several features. R.Ashraf [12] extracted the edge of the image from the Y components after converting the color image to Y. In addition, he combine the result matrices from the three channels: Y-luminance Cb and Cr; and combine them to obtain RGB image.…”

Section: Related Workmentioning

confidence: 99%

Performant Retrieval Image using Rectangular Mask and Combination of Color, Texture and Shape Descriptors

Chifa*¹,

Badri²,

Ruichek³

2019

IJRTE

View full text Add to dashboard Cite

The evolution of computer technologies has led to the growth of digital images, which has made the search for similar images in this volume of data a very important research component. Since several works have proposed image search systems entitled CBIR (Content-Based Image Retrieval). This paper presents a new and powerful method for creating CBIR in order to improve the accuracy of search through visual content. The originality of our method lies in its invariance to the rotation of images queries. She consists of applying rectangular masks of different size on the image, and extracting the color descriptor from the visible region on the mask, and then combining the result descriptor to the Uniform Local Binary Pattern (ULBP) texture features and add canny edge features. We compare the query features to the extract ones, using metric distance. We evaluate our techniques using Corel1K and Ukbench dataset. The average precision measured gives good results comparing to the others existing retrieval systems

show abstract

“…SPM is considered an extension of the order-less BoVW and it addresses the shortcoming of a lack of spatial information [5,37,6]. However, if the objects in the images are flipped or rotated, the discrimination power of SPM decreases [37,15].…”

Section: Introductionmentioning

confidence: 99%

Intelligent image classification-based on spatial weighted histograms of concentric circles

Zafar

Ashraf

Ali

et al. 2018

ComSIS

Self Cite

View full text Add to dashboard Cite

As digital images play a vital role in multimedia content, the automatic classification of images is an open research problem. The Bag of Visual Words (BoVW) model is used for image classification, retrieval and object recognition problems. In the BoVW model, a histogram of visual words is computed without considering the spatial layout of the 2-D image space. The performance of BoVW suffers due to a lack of information about spatial details of an image. Spatial Pyramid Matching (SPM) is a popular technique that computes the spatial layout of the 2-D image space. However, SPM is not rotation-invariant and does not allow a change in pose and view point, and it represents the image in a very high dimensional space. In this paper, the spatial contents of an image are added and the rotations are dealt with efficiently, as compared to approaches that incorporate spatial contents. The spatial information is added by constructing the histogram of circles, while rotations are dealt with by using concentric circles. A weighed scheme is applied to represent the image in the form of a histogram of visual words. Extensive evaluation of benchmark datasets and the comparison with recent classification models demonstrate the effectiveness of the proposed approach. The proposed representation outperforms the state-of-the-art methods in terms of classification accuracy.

show abstract

Content Based Image Retrieval by Using Color Descriptor and Discrete Wavelet Transform

Cited by 98 publications

References 40 publications

Content‐Based Image Retrieval and Feature Extraction: A Comprehensive Review

Content‐Based Image Retrieval and Feature Extraction: A Comprehensive Review

Performant Retrieval Image using Rectangular Mask and Combination of Color, Texture and Shape Descriptors

Intelligent image classification-based on spatial weighted histograms of concentric circles

Contact Info

Product

Resources

About