Content based image retrieval using deep learning process

Saritha, R.; Paul, Varghese; Kumar, P. Ganesh

doi:10.1007/s10586-018-1731-0

Cited by 154 publications

(81 citation statements)

References 33 publications

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“…Furthermore, the deep belief network (DBN) is introduced to use for feature categorization and extraction, which provides a solution for the computational time issue. This method is focused on producing significant results in a short time [6]. In [53] deep learning for image classification is presented by using CNN layers with visual geometry group network (VGG-net) to detect image features and used regularized locality preserving indexing (RLPI) to train the model.…”

Section: Related Workmentioning

confidence: 99%

“…In the modern era, image retrieval using deep learning is a big challenge to retrieve relevant images with highest precision, which still misses the symmetry for feature extraction and description. Convolutional neural network mainly focuses on large image datasets [6] so that CNN is increasing its attention in the computer vision community. Accurate image representation and image feature extraction play a vital role in the image analysis [6].…”

Section: Introductionmentioning

confidence: 99%

“…Convolutional neural network mainly focuses on large image datasets [6] so that CNN is increasing its attention in the computer vision community. Accurate image representation and image feature extraction play a vital role in the image analysis [6]. Moreover, deep learning is also employed for feature learning and 2 of 30 feature description [7].…”

Section: Introductionmentioning

confidence: 99%

“…Deep learning methods are based on the architecture of neural networks and their main objective is to form a feature vector by extracting features and use them for classification problems. Many researchers have focused on various computer vision problems including object detection [20,21], human pose estimation [22,23], motion tracking [24,25], semantic segmentation [26,27] and action recognition [28,29], while according to the applicability, the most famous and important types of the deep learning models are deep Boltzmann machines [30,31], convolutional neural networks, stacked auto-encoders [32] and deep belief networks [6]. Deep learning features for image retrieval are crucial in nature for large datasets because they possess the signatures for texture and spatial and recognition attributes, which are classified and indexed and directly impact the precision.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Deep Learning Using Symmetry, FAST Scores, Shape-Based Filtering and Spatial Mapping Integrated with CNN for Large Scale Image Retrieval

et al. 2020

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This article presents symmetry of sampling, scoring, scaling, filtering and suppression over deep convolutional neural networks in combination with a novel content-based image retrieval scheme to retrieve highly accurate results. For this, fusion of ResNet generated signatures is performed with the innovative image features. In the first step, symmetric sampling is performed on the images from the neighborhood key points. Thereafter, the rotated sampling patterns and pairwise comparisons are performed, which return image smoothing by applying standard deviation. These values of smoothed intensity are calculated as per local gradients. Box filtering adjusts the results of approximation of Gaussian with standard deviation to the lowest scale and suppressed by non-maximal technique. The resulting feature sets are scaled at various levels with parameterized smoothened images. The principal component analysis (PCA) reduced feature vectors are combined with the ResNet generated feature. Spatial color coordinates are integrated with convolutional neural network (CNN) extracted features to comprehensively represent the color channels. The proposed method is experimentally applied on challenging datasets including Cifar-100 (10), Cifar-10 (10), ALOT (250), Corel-10000 (10), Corel-1000 (10) and Fashion (15). The presented method shows remarkable results on texture datasets ALOT with 250 categories and fashion (15). The proposed method reports significant results on Cifar-10 and Cifar-100 benchmarks. Moreover, outstanding results are obtained for the Corel-1000 dataset in comparison with state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Learning Using Symmetry, FAST Scores, Shape-Based Filtering and Spatial Mapping Integrated with CNN for Large Scale Image Retrieval

et al. 2020

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“…The deep learning techniques allow for a wide range of their applications and better solutions for many problems in the computer vision domain, as shown by [25][26][27][28][29][30][31][32][33][34][35], and others. All of these works recommend the use of deep learning techniques to find better solutions in the area of computer optics, in the computer vision domain, which is the main motivation for using the deep learning technique in this paper.…”

Section: Related Workmentioning

confidence: 99%

Predicting Compressive Strength of Cement-Stabilized Rammed Earth Based on SEM Images Using Computer Vision and Deep Learning

et al. 2019

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Predicting the compressive strength of cement-stabilized rammed earth (CSRE) using current testing machines is time-consuming and costly and may harm the environment due to the samples’ waste. This paper presents an automatic method using computer vision and deep learning to solve the problem. For this purpose, a deep convolutional neural network (DCNN) model is proposed, which was evaluated on a new in-house scanning electron microscope (SEM) image database containing 4284 images of materials with different compressive strengths. The experimental results show reasonable prediction results compared to other traditional methods, achieving 84% prediction accuracy and a small (1.5) oot Mean Square Error (RMSE). This indicates that the proposed method (with some enhancements) can be used in practice for predicting the compressive strength of CSRE samples.

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Optimization and Deep Learning–Based Content Retrieval, Indexing, and Metric Learning Approach for Medical Images

Suresh¹,

Sundaresan²,

Raghavan³

et al. 2021

Computational Analysis and Deep Learning for Medical Care

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Content based image retrieval using deep learning process

Cited by 154 publications

References 33 publications

Deep Learning Using Symmetry, FAST Scores, Shape-Based Filtering and Spatial Mapping Integrated with CNN for Large Scale Image Retrieval

Deep Learning Using Symmetry, FAST Scores, Shape-Based Filtering and Spatial Mapping Integrated with CNN for Large Scale Image Retrieval

Predicting Compressive Strength of Cement-Stabilized Rammed Earth Based on SEM Images Using Computer Vision and Deep Learning

Optimization and Deep Learning–Based Content Retrieval, Indexing, and Metric Learning Approach for Medical Images

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