Content-Based Image Retrieval Approach Using Color and Texture Applied to Two Databases (Coil-100 and Wang)

Aroussi, El Mehdi El; Houssif, Noureddine El; Satori, Hassan

doi:10.1007/978-3-319-76357-6_5

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…For the Corel-1000 dataset, the results show that the proposed system is more efficient in terms of average precision than the other systems, with the average precision obtained being 95.92%. The mAP values obtained for the other approaches are 91.87% for the AlexNet CNN [49] approach, 80.61%, 95.80%, 66.5% and 73.27% respectively for the HOG + SURF approaches [50], [53], [51] and [03]. On the FP dataset (Caltech-101) also reflect a trend similar to that obtained for the Wang and Corel-1000 datasets.…”

Section: Comparison Results Of Various Approachessupporting

confidence: 69%

“…In this part, the descriptor efficiencies proposed using the Coil100, Wang, OT and FP databases were estimated and evaluated with other descriptors for CBIR. In this regard, the technique is compared to [33,49,50,51,52,53]. All the experiments below were carried out under the same conditions.…”

Section: Comparison Results Of Various Approachesmentioning

confidence: 99%

“…For the Coil-100 dataset, our proposed method give higher recovery performance for which the average accuracy is 98.23%. The results obtained from the average precision for approach [53] and for approach [51] are respectively 81% and 95%.…”

Section: Comparison Results Of Various Approachesmentioning

confidence: 99%

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A Mighty Image Retrieval Descriptor Based on Machine Learning and Gaussian Derivative Filter

El Mehdi,

Latifa,

Hassan

2024

Int. j. electr. comput. eng. syst. (Online)

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The development of new image descriptor has always been an important topic to improve the efficiency of content- based image classification and retrieval. Improvements and developments in machine learning and deep learning algorithms as well as artificial intelligence algorithms are widely used by researchers to obtain effective CBIR descriptors. In our article, we will present a robust image descriptor, extended by machine learning and deep learning algorithms. The descriptor is provided through a Gaussian derivative filter scaffold named GDF-HOG with an enhanced convolutional neural network (CNN) AlexNet, to reduce the dimensions we used the principal component analysis algorithm. The experimental results were carried out on Oliva and Torralba, Caltech-101, Wang and Coil100 datasets. Experiments show that the accuracy of the proposed method is 98.23% for Coil-100%, 95.92% for Corel-1000, value 87.17 and 94.6% for Oliva and Torralba. In comparison our results with other descriptor image classifiers show that they achieved accuracy increases of 0.12% on average and up to 3.23%. These experimental results affirm the advantage of the proposed descriptor over existing systems based in terms of average accuracy. the proposed descriptor improves the precision, and also reduces the complexity of the calculation.

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Section: Comparison Results Of Various Approachessupporting

confidence: 69%

Section: Comparison Results Of Various Approachesmentioning

confidence: 99%

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A Mighty Image Retrieval Descriptor Based on Machine Learning and Gaussian Derivative Filter

El Mehdi,

Latifa,

Hassan

2024

Int. j. electr. comput. eng. syst. (Online)

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“…The basic necessity of any CBIR system is the brilliant selection of image parameters needed for extraction of features [7]. Color and edge directivity descriptor (CEDD) is utilized for the extraction of both color as well as texture features while a 2nd level of Discrete wavelet transform (DWT) is employed for the analysis of shape features.…”

Section: B Related State-of-the-art Workmentioning

confidence: 99%

An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System

Shikha¹,

Gitanjali²,

Kumar³

2020

IJIMAI

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The process of searching, indexing and retrieving images from a massive database is a challenging task and the solution to these problems is an efficient image retrieval system. In this paper, a unique hybrid Content-based image retrieval system is proposed where different attributes of an image like texture, color and shape are extracted by using Gray level co-occurrence matrix (GLCM), color moment and various region props procedure respectively. A hybrid feature matrix or vector (HFV) is formed by an integration of feature vectors belonging to three individual visual attributes. This HFV is given as an input to an Extreme learning machine (ELM) classifier which is based on a solitary hidden layer of neurons and also is a type of feed-forward neural system. ELM performs efficient class prediction of the query image based on the pre-trained data. Lastly, to capture the high level human semantic information, Relevance feedback (RF) is utilized to retrain or reformulate the training of ELM. The advantage of the proposed system is that a combination of an ELM-RF framework leads to an evolution of a modified learning and intelligent classification system. To measure the efficiency of the proposed system, various parameters like Precision, Recall and Accuracy are evaluated. Average precision of 93.05%, 81.03%, 75.8% and 90.14% is obtained respectively on Corel-1K, Corel-5K, Corel-10K and GHIM-10 benchmark datasets. The experimental analysis portrays that the implemented technique outmatches many state-of-the-art related approaches depicting varied hybrid CBIR system.

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“…Such an indexing based hybrid system has been proposed by Arousi et. al [15] where 2D histogram with statistical moments has been used for color feature extraction and Gray level co-occurance matrix (GLCM) for texture analysis.…”

Section: State -Of-the-art Related Workmentioning

confidence: 99%

A Genesis of a Meticulous Fusion based Color Descriptor to Analyze the Supremacy between Machine Learning and Deep learning

Bhardwaj¹,

Pandove²,

Dahiya³

2020

IJISA

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The tremendous advancements in digital technology pertaining to diverse application areas like medical diagnostics, crime detection, defense etc., has led to an exceptional increase in the multimedia image content. This bears an acute requirement of an effectual retrieval system to cope up with the human demands. Therefore, Content-based image retrieval (CBIR) is among the renowned retrieval systems which uses color, texture, shape, edge and other spatial information to extract the basic image features. This paper proposes an efficient and unexcelled hybrid color descriptor which is an amalgamation of color histogram, color moment and color auto-correlogram. In order to determine the predominance between machine learning and deep learning, two machine learning models, Support vector machine (SVM) and Extreme learning machine (ELM) have been tested. Whereas from deep learning category, Cascade forward back propagation neural network (CFBPNN) and Patternnet have been utilized. Finally, from these divergent tested algorithms, CFBPNN attains the highest accuracy and has been selected to enhance the retrieval accuracy of the proposed system. Numerous standard benchmark datasets namely Corel-1K, Corel-5K, Corel-10K, Oxford flower, Coil-100 and Zurich buildings have been tested here and average precision of 97.1%, 90.3%, 87.9%, 98.4%, 98.9% and 82.7% is obtained respectively which is significantly higher than many state-of-the-art related techniques.

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Content-Based Image Retrieval Approach Using Color and Texture Applied to Two Databases (Coil-100 and Wang)

Cited by 9 publications

References 14 publications

A Mighty Image Retrieval Descriptor Based on Machine Learning and Gaussian Derivative Filter

A Mighty Image Retrieval Descriptor Based on Machine Learning and Gaussian Derivative Filter

An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System

A Genesis of a Meticulous Fusion based Color Descriptor to Analyze the Supremacy between Machine Learning and Deep learning

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