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Journal ArticleDOI

Multi-Resolution Joint Auto Correlograms for Content-Based Image Retrieval

01 Jun 2017-Advanced Science Letters-Vol. 23, Iss: 6, pp 5370-5374

AboutThis article is published in Advanced Science Letters.The article was published on 2017-06-01 and is currently open access. It has received 4 citation(s) till now. The article focuses on the topic(s): Content-based image retrieval.

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Summary

  • The past few years have seen a major development in Content-based Image Retrieval (CBIR) due to the needs by various fields in accessing visual data, particularly images.
  • Colour Auto Correlogram (CAC) is one of the promising colour descriptors used to extract and index image features effectively.
  • The conventional CAC and most of its advancements are only based on single image feature, not sensitive to scale, and computed in the spatial domain.
  • A new method for CBIR has been introduced by allowing multiple local image features to represent an image rather than just colour and extracting them at different level of the image sub-bands to provide different physical structures of the image in the frequency domain.
  • The Ridgelet transform is performed on the RGB colour space and the greyscale version of the image to provide the multi-resolution levels.
  • Colour feature is extracted from the Ridgelet coefficients of the RGB colour space while other image features such as gradient magnitude, rank, and texturedness are extracted from the Ridgelet coefficients of the grey-scale image.
  • Each of these image features is quantised and the auto correlogram is then performed on the respective quantised image feature coefficients.
  • Retrieval experiments conducted on 1000 SIMPLIcity image database has demonstrated that the proposed Multiresolution Joint Auto Correlograms (MJAC) has achieved a significant improvement in effectiveness compared to the traditional CAC and several of its advancements.
  • CAC; CBIR; Colour descriptor; MJAC; Ridgelet transform, also known as Keyword.

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Multi-resolution joint auto correlograms for content-based image retrieval
ABSTRACT
The past few years have seen a major development in Content-based Image Retrieval (CBIR)
due to the needs by various fields in accessing visual data, particularly images. As a result,
several techniques have been developed to allow image databases to be queried by their image
content. Colour Auto Correlogram (CAC) is one of the promising colour descriptors used to
extract and index image features effectively. However, the conventional CAC and most of its
advancements are only based on single image feature, not sensitive to scale, and computed in
the spatial domain. A new method for CBIR has been introduced by allowing multiple local
image features to represent an image rather than just colour and extracting them at different
level of the image sub-bands to provide different physical structures of the image in the
frequency domain. The Ridgelet transform is performed on the RGB colour space and the grey-
scale version of the image to provide the multi-resolution levels. Colour feature is extracted
from the Ridgelet coefficients of the RGB colour space while other image features such as
gradient magnitude, rank, and texturedness are extracted from the Ridgelet coefficients of the
grey-scale image. Each of these image features is quantised and the auto correlogram is then
performed on the respective quantised image feature coefficients. Retrieval experiments
conducted on 1000 SIMPLIcity image database has demonstrated that the proposed Multi-
resolution Joint Auto Correlograms (MJAC) has achieved a significant improvement in
effectiveness compared to the traditional CAC and several of its advancements.
Keyword: CAC; CBIR; Colour descriptor; MJAC; Ridgelet transform
Citations
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Journal ArticleDOI
TL;DR: This work aims to contribute to a new representation method based on multiple features for effective durian recognition, and two features based on shape and texture is considered in this work.
Abstract: Many fruit recognition approaches today are designed to classify different type of fruits but there is little effort being done for content-based fruit recognition specifically focuses on durian species. Durian, known as the king of tropical fruits, have few similar characteristics between different species where the skin have almost the same colour from green to yellowish brown with just slightly different shape and pattern of thorns. Therefore, it is hard to differentiate them with the current methods. It would be valuable to have an automated content-based recognition framework that can automatically represent and recognise a durian species given a durian image as the input. Therefore, this work aims to contribute to a new representation method based on multiple features for effective durian recognition. Two features based on shape and texture is considered in this work. Simple shape signatures which include area, perimeter, and circularity are used to determine the shape of the fruit durian and its base while the texture of the fruit is constructed based on Local Binary Pattern. We extracted these features from 240 durian images and trained this proposed method using few classifiers. Based on 10-fold cross validation, it is found that Logistic Regression, Gaussian Naive Bayesian, and Linear Discriminant Analysis classifiers performed equally well with 100% achievement of accuracy, precision, recall, and F1-score. We further tested the proposed algorithm on larger dataset which consisted of 42337 fruit images (64 various categories). Experimental results based on larger and more general dataset have shown that the proposed multiple features trained on Linear Discriminant Analysis classifier able to achieve 72.38% accuracy, 73% precision, 72% recall, and 72% F1-score.

4 citations


Cites background from "Multi-Resolution Joint Auto Correlo..."

  • ...Feature extraction is very important where the right features will lead to an accurate representation and recognition, and vice versa [10]....

    [...]


Proceedings ArticleDOI
26 Mar 2018
TL;DR: This work aims to contribute to an automatic content-based durian species recognition that will be able to assist users in differentiating various species of durian.
Abstract: Many fruit recognition systems today are designed to classify different type of fruits but there is no content-based fruit recognition system focuses on durian species. Durian, known as the king of tropical fruits, have few similar characteristics between different species where the skin have almost the same color from green to yellowish brown with slightly different shape of thorns and it is hard to differentiate them with the current methods. Sometimes it is even hard for general consumers to differentiate durian species by themselves. This work aims to contribute to an automatic content-based durian species recognition that will be able to assist users in differentiating various species of durian. Few global contour-based and region-based shape descriptors such as area, perimeter, and circularity are computed as feature vectors and K-Nearest Neighbors algorithm is used to classify the durian based on the extracted features. 10-fold cross-validation is used to evaluate the proposed system. Experimental results have shown that the proposed feature extraction method for the durian species recognition system has successfully obtained a positive recognition rate of 100%.

3 citations


Cites background from "Multi-Resolution Joint Auto Correlo..."

  • ...Feature extraction is very important in image representation where the right features will lead to accurate representation and recognition, and vice versa [10]....

    [...]


Journal ArticleDOI
01 May 2019
TL;DR: Different methods, namely Local Color Histogram, Color Correlogram, Row sum and Column sum and Colors Coherences Vectors were used to extract colors features taking in consideration the spatial information of the image.
Abstract: Inn then last two decades the Content Based Image Retrieval (CBIR) considered as one of the topic of interest for theresearchers. It depending one analysis of the image’s visual content which can be done by extracting the color, texture and shapefeatures. Therefore, feature extraction is one of the important steps in CBIR system for representing the image completely. Color featureis the most widely used and more reliable feature among the image visual features. This paper reviews different methods, namely LocalColor Histogram, Color Correlogram, Row sum and Column sum and Colors Coherences Vectors were used to extract colors featurestaking in consideration the spatial information of the image.

1 citations


Cites background from "Multi-Resolution Joint Auto Correlo..."

  • ...images It is slow computations and high dimensionality [22] [23] [24]...

    [...]


Journal ArticleDOI
TL;DR: The results show that the proposed algorithm works well and can be applied to practice and can provide theoretical reference for subsequent related research.
Abstract: The huge amount of digital image data in e-commerce transactions brings serious problems to the rapid retrieval and storage of images. Image hashing technology can convert image data of arbitrary resolution into a binary code sequence of tens or hundreds of bits through a hash function. In view of this, based on the image content characteristics, this study improved the traditional hash function and proposed a hash method based on bilateral random projection. At the same time, the projection vectors are acquired in the low-rank sparse decomposition process of the image data matrix, and the projection vectors are group orthogonalized. In addition, this study designed contrast test to carry out research and analysis on the effectiveness of the algorithm. The results show that the proposed algorithm works well and can be applied to practice and can provide theoretical reference for subsequent related research.

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Q1. What are the contributions in this paper?

A new method for CBIR has been introduced by allowing multiple local image features to represent an image rather than just colour and extracting them at different level of the image sub-bands to provide different physical structures of the image in the frequency domain. The Ridgelet transform is performed on the RGB colour space and the greyscale version of the image to provide the multi-resolution levels.