Color Object Detection Based Image Retrieval Using ROI Segmentation with Multi-Feature Method

Raja, Rohit; Kumar, Sandeep; Mahmood, Rashid

doi:10.1007/s11277-019-07021-6

Cited by 52 publications

(15 citation statements)

References 15 publications

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“…e experimental evaluation and analysis illustrate that the implemented technique outstrips many state-of-the-art related approaches based on varied hybrid systems. e proposed research achieves the highest accuracy as compared to the state-of-the-art research, thereby outperforming the researches of Li et al [61], Aslam et al [14], SCNN-ELM [61], MKSVM-MIL et al [62], Raja et al [41], Desai et al [42], Yu et al [44], and Shikha et al [43] by 26.16%, 15.74%, 12.68%, 11.8%, 10.34%, 8.8%, 1.02%, and 0.5%, respectively.…”

Section: Results For Corel-1k Image Datasetmentioning

confidence: 73%

“…Classification accuracy (%) Inception-V3 [46] 91.1 Feature RCG SVM [60] 93.81 AlexNet [46] 94.2 GoogLeNet [39] 94.31 CaffeNet [39] 95.02 VGG-VD-16 [39] 95.21 ResNet50 97.78 Name of algorithm/model Classification accuracy (%) Li et al [61] 70.84 Aslam et al [14] 81.26 SCNN-ELM [61] 84.32 MKSVM-MIL et al [62] 85.2 Raja et al [41] 86.66 Desai et al [42] 88.2 Yu et al [44] 95.98 Shikha et al [43] 96.5 ResNet50 97…”

Section: Name Of Algorithm/modelmentioning

confidence: 99%

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Satellite and Scene Image Classification Based on Transfer Learning and Fine Tuning of ResNet50

Shabbir

Ali

Ahmed

et al. 2021

Mathematical Problems in Engineering

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Image classification has gained lot of attention due to its application in different computer vision tasks such as remote sensing, scene analysis, surveillance, object detection, and image retrieval. The primary goal of image classification is to assign the class labels to images according to the image contents. The applications of image classification and image analysis in remote sensing are important as they are used in various applied domains such as military and civil fields. Earlier approaches for remote sensing images and scene analysis are based on low-level feature representations such as color- and texture-based features. Vector of Locally Aggregated Descriptors (VLAD) and orderless Bag-of-Features (BoF) representations are the examples of mid-level approaches for remote sensing image classification. Recent trends for remote sensing and scene classification are focused on the use of Convolutional Neural Network (CNN). Keeping in view the success of CNN models, in this research, we aim to fine-tune ResNet50 by using network surgery and creation of network head along with the fine-tuning of hyperparameters. The learning of hyperparameters is tuned by using a linear decay learning rate scheduler known as piecewise scheduler. To tune the optimizer hyperparameter, Stochastic Gradient Descent with Momentum (SGDM) is used with the usage of weight learn and bias learn rate factor. Experiments and analysis are conducted on five different datasets, that is, UC Merced Land Use Dataset (UCM), RSSCN (the remote sensing scene classification image dataset), SIRI-WHU, Corel-1K, and Corel-1.5K. The analysis and competitive results exemplify that our proposed image classification-based model can classify the images in a more effective and efficient manner as compared to the state-of-the-art research.

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Section: Results For Corel-1k Image Datasetmentioning

confidence: 73%

Section: Name Of Algorithm/modelmentioning

confidence: 99%

Satellite and Scene Image Classification Based on Transfer Learning and Fine Tuning of ResNet50

Shabbir

Ali

Ahmed

et al. 2021

Mathematical Problems in Engineering

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“…When using SVM, ANN, and CNN for Digit Recognition, the mixture issue is specific heuristic laws. In certain instances, the indefinite duration of the digits or photos renders the job more complex for classifiers to recognize [49], [50]. However, through integrating HVP and CA, the suggested framework enables the automated handwriting of digits on registered photos to be confused with machine learning techniques.…”

Section: Discussionmentioning

confidence: 99%

Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques

Raja

Gupta

Miri

2021

EJERS

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The purpose of this study is to create an automated framework that can recognize similar handwritten digit strings. For starting the experiment, the digits were separated into different numbers. The process of defining handwritten digit strings is then concluded by recognizing each digit recognition module's segmented digit. This research utilizes various machine learning techniques to produce a strong performance on the digit string recognition challenge, including SVM, ANN, and CNN architectures. These approaches use SVM, ANN, and CNN models of HOG feature vectors to train images of digit strings. Deep learning methods organize the pictures by moving a fixed-size monitor over them while categorizing each sub-image as a digit pass or fail. Following complete segmentation, complete recognition of handwritten digits is accomplished. To assess the methods' results, data must be used for machine learning training. Following that, the digit data is evaluated using the desired machine learning methodology. The Experiment findings indicate that SVM and ANN also have disadvantages in precision and efficiency in text picture recognition. Thus, the other process, CNN, performs better and is more accurate. This paper focuses on developing an effective system for automatically recognizing handwritten digits. This research would examine the adaptation of emerging machine learning and deep learning approaches to various datasets, like SVM, ANN, and CNN. The test results undeniably demonstrate that the CNN approach is significantly more effective than the ANN and SVM approaches, ranking 71% higher. The suggested architecture is composed of three major components: image pre-processing, attribute extraction, and classification. The purpose of this study is to enhance the precision of handwritten digit recognition significantly. As will be demonstrated, pre-processing and function extraction are significant elements of this study to obtain maximum consistency.

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“…In FFNN, only the input and output mapping is performed directly but not aligned with the previous input. However, the next coming scheme call series arranged in a gathering of particular call series and this series cannot be anything [20][21]. For instance, two series like (fstat, old_ mmap, close) and (execve, uname,brk) are valid series and meaningless due to the requirement of opening a file before making any effort to close.…”

Section: Fig 4 Feed-forward Networkmentioning

confidence: 99%

An Effective Contour Detection based Image Retrieval using Multi-Fusion Method and Neural Network

Raja

Kumar

Choudhary³

et al. 2021

Preprint

Self Cite

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Day by day, rapidly increasing the number of images on digital platforms and digital image databases has increased. Generally, the user requires image retrieval and it is a challenging task to search effectively from the enormous database. Mainly content-based image retrieval (CBIR) algorithm considered the visual image feature such as color, texture, shape, etc. The non-visual features also play a significant role in image retrieval, mainly in the security concern and selection of image features is an essential issue in CBIR. Performance is one of the challenging tasks in image retrieval, according to current CBIR studies. To overcome this gap, the new method used for CBIR using histogram of gradient (HOG), dominant color descriptor (DCD) & hue moment (HM) features. This work uses color features and shapes texture in-depth for CBIR. HOG is used to extract texture features. DCD on RGB and HSV are used to improve efficiency and computation. A neural network (NN) is used to extract the image features, which improves the computation using the Corel dataset. The experimental results evaluated on various standard benchmarks Corel-1k, Corel-5k datasets, and outcomes of the proposed work illustrate that the proposed CBIR is efficient for other state-of-the-art image retrieval methods. Intensive analysis of the proposed work proved that the proposed work has better precision, recall, accuracy

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Color Object Detection Based Image Retrieval Using ROI Segmentation with Multi-Feature Method

Cited by 52 publications

References 15 publications

Satellite and Scene Image Classification Based on Transfer Learning and Fine Tuning of ResNet50

Satellite and Scene Image Classification Based on Transfer Learning and Fine Tuning of ResNet50

Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques

An Effective Contour Detection based Image Retrieval using Multi-Fusion Method and Neural Network

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