Content-based image retrieval from videos using CBIR and ABIR algorithm

Wankhede, V. A.; Mohod, P. S.

doi:10.1109/gcct.2015.7342767

Cited by 9 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first method uses text-mode to retrieve the video in a dataset. The second method uses contents in video frame, which are color, shape, texture to retrieve the video in a dataset [3]. Many researchers conducted on many experiments with many methods, which have the following brief details.…”

Section: Content-based Video Retrieval (Cbvr)mentioning

confidence: 99%

Image-to-Video Retrieval by ResNet50

Pornpanomchai

2023

ijmst

View full text Add to dashboard Cite

The objective of this research is to create a computer system which can retrieve a video clip by using only a single image. The developed system is called “Image-to-Video Retrieval System (I2VRS)”. The system employs the convolutional neural networks called “ResNet50”, which is a toolbox in MATLAB software to retrieve the video clip dataset. The ResNet50 is one of the powerful CNN to recognize an image in the image processing technique. The I2VRS creates its own dataset called I2VRS dataset, which consists of 101 video clips and each video clip contains 1,000 video frames. All video clips are filmed around 60 s. each in the .MP4 file-format. The system also tests an un-training dataset with 100 images, which are directly taken with a mobile phone in the .JPEG file-format. The mean average precision (mAP) of the system is 0.9825, with the training dataset time being 5,668.7 s. The average access time to retrieve a video clip is 1.5726 s. per image.

show abstract

Section: Content-based Video Retrieval (Cbvr)mentioning

confidence: 99%

Image-to-Video Retrieval by ResNet50

Pornpanomchai

2023

ijmst

View full text Add to dashboard Cite

show abstract

“…CBIR systems are projected on many techniques. But there are some problems withthe image retrieving system based on pixel contents [68][69][70][71][72]. Early research works on content-based image retrieval techniques are summarized here for the reader's attention.…”

Section: Content-based Image Retrieval Techniquesmentioning

confidence: 99%

An analysis of content-based image retrieval

Koyuncu

Dixit²,

Koyuncu

2021

International Advanced Researches and Engineering Journal

View full text Add to dashboard Cite

Nowadays, working on digital images is gaining much popularity in multimedia systems, due to the rapid increase in the utilization of large image databases. Thus, the Content-Based Image Retrieval (CBIR) method has become the most valuable method for these databases. This study mainly focuses on content-based image retrieval; which uses image features like color, shape, texture, etc. by searching the user query image from a large image database based on user request. CBIR is the most widely used technique as its searching capability is faster than the other traditional methods, and it works well in retrieving images automatically. It is also a big alternative approach to traditional methods. The CBIR techniques are used in many applications like surveillance detection, crime avoidance, fingerprint identification, E-library, medical, historical monument and biodiversity information systems, and many more. A total of 38 CBIR articles were comparatively analyzed.

show abstract

“…Large amounts of the videos are acquired then stored on the computer devices because of the widespread uses of the digital video devices in a variety of the regions. The capability for the manipulation and access of the stored videos is commonly utilized by the users of various domains in a variety of the ways [3,4]. The systems of video retrieval have gained great importance as a result of an increasing amount of the visual data for the videos [5].…”

Section: Introductionmentioning

confidence: 99%

Advanced Evaluation Method for Video Retrieval System

Abdulateef

2023

IJASRE

View full text Add to dashboard Cite

n the past few years, the multimedia storage has increased and costs of storing multi-media data has become cheaper. Which is why, there are large numbersof videos that are available in the video repositories. With development of the multi-media data types and available bandwidths.The proposed method transferseach video of database into scenes using color histogrambased scene change detection algorithm and key framesare extracted. For key frames multiple features areobtained using straight forward rules. A new framework depending on CNNs (convolutional neural networks) is recommended to perform the content based video retrieval with the less storage cost also with higher search capability. The recommended framework subsists of extraction algorithm with respect to key-frame and the feature collection strategies. Particularly, the extraction algorithm of key-frame takes benefit of clustering idea; so in that case excessive information is taken out from video data and also the storage cost is highly shortened. This work present a method uses the extracted features with convolutional neural network (CNN) for classification tasks. In this research paper, different types of videos will be used and the important features will be extracted using SIFT, after that we use the deep learning(CNN) process will be performed. The experimental results showed the efficiency and effectiveness of suggested approach

show abstract

Content-based image retrieval from videos using CBIR and ABIR algorithm

Cited by 9 publications

References 7 publications

Image-to-Video Retrieval by ResNet50

Image-to-Video Retrieval by ResNet50

An analysis of content-based image retrieval

Advanced Evaluation Method for Video Retrieval System

Contact Info

Product

Resources

About