Efficient Image Retrieval through Hybrid Feature Set and Neural Network

Arora, Nitin; Ashok, Alaknanda; Tiwari, Shamik

doi:10.5815/ijigsp.2019.01.05

Cited by 11 publications

(2 citation statements)

References 12 publications

(21 reference statements)

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“…Nitin Arora et al, [25] have used Haar wavelet features and Gabor features. They have shown that by using hybrid features some improvement in the retrieval accuracy can be obtained.…”

Section: Existing Work In Cbirmentioning

confidence: 99%

A Novel Technique for Image Retrieval based on Concatenated Features Extracted from Big Dataset Pre-Trained CNNs

Bhuma¹,

Ramanjaneyulu²

2023

IJIGSP

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Accessing semantically relevant data from a database is not only essential in commercial applications but also in medical imaging diagnosis. Representation of the query image by its features and subsequently the dataset are the key factors in Content Based Image Retrieval (CBIR). Texture, shape and color are commonly used features for this purpose. Features extracted from the pre-trained Convolutional Neural Networks (CNNs) are used to improve the performance of CBIR methods. In this work, we explore a recent state of the art big dataset pre-trained CNNs which are known as Big Transfer Networks. Features extracted from Big Transfer Network have higher discriminative power compared to features of many other pre-trained CNNs. The idea behind the proposed work is to demonstrate the effectiveness of using features of big transfer networks for image retrieval. Further, features extracted from big transfer networks are concatenated to improve the performance of the proposed method. Feature diversity supplemented with network diversity should ensure good discriminative power for image retrieval. This idea is supported by performing simulations on four datasets with varying sizes in terms of number of images and classes. As feature size increases with the concatenation, we applied a dimensionality reduction algorithm i.e., Principal Component Analysis. Several distance metrics are explored in this work. By properly choosing the pre-trained CNNs and distance metric, it is possible to achieve higher mean average precisions. ImageNet-21K pre-trained CNN and Instagram pre-trained CNN are chosen in this work. Further, a pre-trained network trained on Imagenet-21K dataset is superior compared to the networks trained on ImageNet-1K dataset as there are more classes and presence of wide variety of images. This is demonstrated by applying our algorithm on four datasets i.e., COREL-100, CALTECH-101, FLOWER-17 and COIL-100. Simulations are presented for various precisions (scopes), and distance metrics. Results are compared with the existing algorithms and superiority of the proposed method in terms of mean Average Precision is shown.

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“…Nitin Arora et al, [25] have used Haar wavelet features and Gabor features. They have shown that by using hybrid features some improvement in the retrieval accuracy can be obtained.…”

Section: Existing Work In Cbirmentioning

confidence: 99%

A Novel Technique for Image Retrieval based on Concatenated Features Extracted from Big Dataset Pre-Trained CNNs

Bhuma¹,

Ramanjaneyulu²

2023

IJIGSP

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show abstract

“…Then, top N images are retrieved after this step. The prominent distance metrics[33] which are utilized in the proposed technique are given as under:…”

mentioning

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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Inherited Bio-symmetries and Algebraic Harmony in Genomes of Higher and Lower Organisms

Petoukhov

Petukhova

Svirin

et al. 2021

Advances in Intelligent Systems and Computing

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Efficient Image Retrieval through Hybrid Feature Set and Neural Network

Cited by 11 publications

References 12 publications

A Novel Technique for Image Retrieval based on Concatenated Features Extracted from Big Dataset Pre-Trained CNNs

A Novel Technique for Image Retrieval based on Concatenated Features Extracted from Big Dataset Pre-Trained CNNs

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

Inherited Bio-symmetries and Algebraic Harmony in Genomes of Higher and Lower Organisms

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