An Efficient System for Color Image Retrieval Representing Semantic Information to Enhance Performance by Optimizing Feature Extraction

Kumari, Bandna; Kumar, Rohit; Singh, Vijay Kumar; Pawar, Lokesh; Pandey, Pankaj; Sharma, Manish

doi:10.1016/j.procs.2019.05.032

Cited by 11 publications

(2 citation statements)

References 8 publications

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“…Hue Saturation Value (HSV) (33) Histogram is used in the feature extraction stage to separate the input image into textured class and non-textured class. HSV represent the colour format to describe the colours by their shade, saturation, and brightness https://www.indjst.org/ value.…”

Section: Hsv Histogrammentioning

confidence: 99%

Multi-dimensional CNN Based Feature Extraction with Feature Fusion and SVM for Human Activity Recognition in Surveillance Videos

Shah,

Holia

2024

IJST

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Background/Objectives: The accurate recognition of human activities from video sequences is very challenging due to low resolution, cluttered background, partial occlusion, and different viewpoints. Machine learning (ML) based automated HAR from surveillance videos is required with the fusion of various feature extraction techniques. Methods: In this paper, SVM with feature fusion is utilized for automatic recognition from surveillance videos. A Histogram of Oriented Gradient (HOG) is used to segment the frame to differentiate humans from other objects or background noise in the input video frames. The multi-feature extraction can be accomplished in terms of Gabor Wavelet Transform (GWT), Autocorrelogram, Gray-Level Co-Occurrence Matrix (GLCM), HSV histogram, and Multi-dimensional CNN. The proposed approach is implemented in MATLAB software and compared with existing approaches like Space-Time Interest Point (STIP) and Histogram of Optical Flow (HOF). Findings: The proposed approach outperforms the existing approaches in terms of reduced time consumption and high accuracy, 99.886% when using the UCF101 dataset and 99.538% when using the UTKinect dataset. Novelty: The most discriminative feature information is obtained with the feature-level fusion technique. From the feature information, various human actions are recognized with the classification algorithm. Keywords: Human activity recognition, Machine Learning, Surveillance Videos, Human detection algorithm, Feature extraction, SVM classifier

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Section: Hsv Histogrammentioning

confidence: 99%

Multi-dimensional CNN Based Feature Extraction with Feature Fusion and SVM for Human Activity Recognition in Surveillance Videos

Shah,

Holia

2024

IJST

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“…However, there is no quantitative numerical standard for the evaluation of edge detection results, which is often judged by the subjective evaluation method of the naked eyes [9][10] [11] .Due to the subjectivity of subjective evaluation methods, even for the same test results, the evaluation results are often different in different environments and different atmospheres [12][13] [14] .Therefore, a specific quantitative numerical index to evaluate the quality of the detection results is needed to be establish. Generally speaking, for mainstream computer vision, the results of edge detection should meet the following requirements, namely, canny proposed three criteria of edge detection in 1986 [15][16] [17] [18] .…”

Section: Introductionmentioning

confidence: 99%

A New Objective Evaluation Method on Image Edge Detection

Chen,

Liu

2023

2023 3rd International Conference on Electronic Information Engineering and Computer Science (EIECS)

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In the field of machine vision, image edge detection is an important technology, which can make necessary preparation for the later image processing. Consequently, accurate evaluation of the edge detection effect of the processed image is a more critical technology. In this paper, the new three criteria are proposed, which is based on Canny's three criteria for edge detection. Through theoretical analysis, three quantitative basic evaluation indexes are given to the new three criteria, namely SSIM index of image reconstruction similarity, BIdx index of edge reliability and CIdx index of edge continuity. Additionally, noise evaluation criteria is also considered. Then the four basic evaluation indexes are fused to get the objective evaluation index EIdx of image edge detection. Finally, the experimental data show that the accuracy of the method is better than that of the subjective evaluation system.

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An efficient comparison of two indexing-based deep learning models for the formation of a web-application based IoT-cloud network

Bhardwaj

Pandove

Dahiya

2020

J Ambient Intell Human Comput

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An Efficient System for Color Image Retrieval Representing Semantic Information to Enhance Performance by Optimizing Feature Extraction

Cited by 11 publications

References 8 publications

Multi-dimensional CNN Based Feature Extraction with Feature Fusion and SVM for Human Activity Recognition in Surveillance Videos

Multi-dimensional CNN Based Feature Extraction with Feature Fusion and SVM for Human Activity Recognition in Surveillance Videos

A New Objective Evaluation Method on Image Edge Detection

An efficient comparison of two indexing-based deep learning models for the formation of a web-application based IoT-cloud network

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