Content based video retrieval using deep learning feature extraction by modified VGG_16

Kumar, B. Satheesh; Seetharaman, K.

doi:10.1007/s12652-022-03869-y

Cited by 7 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…B. Satheesh Kumar et al [4] employ this method for CBVR. It discusses the current difficulties consumers in the multimedia field encounter while trying to go through vast amounts of data to find pertinent items or distinctive photos.…”

Section: Vgg 16mentioning

confidence: 99%

See 1 more Smart Citation

Content Based Video Retrieval using Deep Learning

Palanivelu,

Jagdale,

Dubey

et al. 2024

Preprint

View full text Add to dashboard Cite

Content-based video retrieval (CBVR) is part of a search engine which is widely utilized in everyday life for retrieving relevant images/frames from given large datasets. Generally, the existing methods don't give the relevance and expected images as per the query images and also require more computational time. The CBVR systems depend on the feature representation and similarity measurements. In this project, a CBVR system is developed using deep learning models. The convolutional neural network (CNN) is used to extract features from an input image/frame, and then comparing those features with images in datasets to retrieve similar images/frames. The process involves three main phases namely feature extraction, feature comparison, and retrieval. In feature extraction, the CNN is trained on a dataset of images to learn to extract features that are relevant to the task at hand. In feature comparison, the features extracted from the input image are compared to the features of the images in the database using a similarity measure such as Euclidean distance. Finally, in the retrieval phase, the most similar images/frames from the datasets are returned as the results. The CBVR is carried out using a pre-trained deep learning model, such as VGG16 or ResNet, which is used to train over large image datasets and can extract useful features from new images. In this project, the retrieval of images are carried out and the performance is evaluated based on accuracy, precision, recall. Various CCN models are experimented to perform result analysis. Keywords: Control based video retrieval, Deep learning, Convolutional Neural Network, Information Retrieval, Feature extraction, Inception V3, Feature selection

show abstract

Section: Vgg 16mentioning

confidence: 99%

“…The study by Suneel Kumar et al (2022) [17] describes a framework for feature fusion in a Content-Based Image Retrieval (CBIR) system. The traditional approach involves extracting image characteristics such as color and texture, followed by manual selection for fusion.…”

Section: Discrete Cosine Transformmentioning

confidence: 99%

Content Based Video Retrieval using Deep Learning

Palanivelu,

Jagdale,

Dubey

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Content-based audio information retrieval should be simple and accurate. The so-called simplicity refers to the user's ability to use the retrieval system simply and conveniently, without the need to master the relevant domain expertise [30][31]. Accuracy means that the search results should be as close as possible to the user's search requirements.…”

Section: Multimedia Retrievalmentioning

confidence: 99%

Construction of law and economics litigation service platform based on multimedia retrieval

Cai

2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

Legal economics uses empirical research to analyze and predict the effects of various legal system arrangements to better explain whether the actual effect of the law is consistent with the expected effect of the law. Artificial Intelligence (AI) has become the core driving force of the new round of scientific and technological revolutions, and the trend of cross-border integration is becoming more and more obvious. Therefore, the intelligent construction of a legal litigation platform is the only way for social development. Litigation service management is a platform that uses big data technology to process and facilitate the information development of legal litigation services. In the face of the rapid changes in legal litigation services, an intelligent litigation service management platform is needed to serve the public. Given the above defects, this paper proposes a law and economics litigation service management platform based on multimedia retrieval, including a litigation service management system, a queuing system, and an autonomous operating system. The litigation service management system is connected with the queuing system and autonomous operating system, respectively. We provide a litigation service management platform to provide efficient operations with humanized functions to assist and improve the service quality of legal litigation services. At the same time, we combined the current application status of AI in grassroots courts and the actual work to analyze the difficulties and problems faced, draw lessons from the successful experience of some courts in China, and find out the direction and path for the application and promotion of AI in grassroots courts.

show abstract

STHARNet: spatio-temporal human action recognition network in content based video retrieval

Sowmyayani

Rani

2022

Multimed Tools Appl

View full text Add to dashboard Cite

Content based video retrieval using deep learning feature extraction by modified VGG_16

Cited by 7 publications

References 15 publications

Content Based Video Retrieval using Deep Learning

Content Based Video Retrieval using Deep Learning

Construction of law and economics litigation service platform based on multimedia retrieval

STHARNet: spatio-temporal human action recognition network in content based video retrieval

Contact Info

Product

Resources

About