Content Based Video Retrieval Using SURF Descriptor

Asha, S.; Sreeraj, M

doi:10.1109/icacc.2013.49

Cited by 17 publications

(6 citation statements)

References 10 publications

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“…Published by Herbert Bay in 2006 [1], this algorithm is fast enough to detect the features in an image, hence it is suitable to be performed in a video retrieval which contains massive amount of images. Closely related to this research is a work Asha et al [6] that developed a content based video retrieval based on SURF. The result gives a 78% accuracy in detecting objects in videos.…”

Section: Introductionmentioning

confidence: 99%

Implementing Content Based Video Retrieval Using Speeded-Up Robust Features

Tarigan

Sihombing

Marpaung

2019

International Journal of Simulation: Systems, Science &Amp; Technology

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In this paper, we propose an implementation of Content-Based Video Retrieval (CBVR) using Speeded-Up Robust Features (SURF). Given an image as a query, the application looks through a set of videos and pick the ones contain frame similar to the image query. Our objective is to measure the performance of the algorithm. The performance is measured using three variables: recall, precision, and running time. We used two sets of samples to perform the test: in-frame and not-in-frame. Furthermore, we limit the samples only to contain these 5 categories: body parts, kitchen and eating utensils, fruits, and pets. The test shows the program gives a 57.75% average recall value and 37.5% precision value for not-in-frame test, while the in-frame-test gives 51% and 59% for recall and precision value respectively. Moreover, the running time data shows there is no relation between in-frame/not-in-frame and speed. Running time performance highly depends on the image query and the length of the video.

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Section: Introductionmentioning

confidence: 99%

Implementing Content Based Video Retrieval Using Speeded-Up Robust Features

Tarigan

Sihombing

Marpaung

2019

International Journal of Simulation: Systems, Science &Amp; Technology

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“…Objects' interest point matching in videos using SIFT features for objects-based video retrieval is presented in [6]. Similarly, SURF descriptor is used in [7] for video retrieval problem. In order to achieve lower dimensions of SURF descriptors, authors utilized stochastic dimensionality reduction method to have an efficient CBVR system.…”

Section: Introductionmentioning

confidence: 99%

Event-Oriented 3D Convolutional Features Selection and Hash Codes Generation Using PCA for Video Retrieval

et al. 2020

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The extensive video surveillance networks gather an enormous amount of data exponentially on a daily basis and its management is a challenging task, requiring efficient and effective techniques for searching, indexing, and retrieval. The employed mainstream techniques are focusing on general category videos, where the important events in surveillance require fine-grained events retrieval. In this paper, we introduce an event-oriented feature selection mechanism by utilizing the intermediate convolutional layer of a pre-trained 3D-CNN model, that is selected after deep investigation of its weights and response to a particular event. The extracted exclusive features represent an event semantically and effectively eliminate those neurons which do not respond to that event. Furthermore, the event-oriented convolutional features are of very high-dimensions, requiring additional storage, and take more time in feature comparison for retrieval. Therefore, we generate compact binary codes from these features using principle component analysis (PCA) algorithm. This makes our system more efficient to retrieve videos from large scale database. We evaluated our approach on the challenging events of UCF101 and HMDB51 datasets for original features and generated compact codes, achieving faster execution time, and better precision and recall scores. INDEX TERMS Deep learning, feature selection, video retrieval, video analytics, hash codes, surveillance event analysis.

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“…The content based approach focuses on the retrieval of videos by their similarity matching based on its video content. This content can be represented by either: global feature or local feature [4]. Global descriptors detail the overall content of the image but with no information about the spatial distribution of this content.…”

Section: Related Workmentioning

confidence: 99%

Content based Video Retrieval using Enhance Feature Extraction

Patel¹

2015

IJCA

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Videos are a powerful and communicative media that can capture and present information. In recent times, large video databases are created because of the advancements in many video acquiring devices and Internet. A reliable system is needed to automate the process of this large amount of data. Contentbased video retrieval has attracted extensive research during the decades. There are various models used for video retrieval. Content Based Video Retrieval is one model for retrieval of videos. Different users have different results in their minds. These lead to the process of selecting, indexing and ranking the database according to the human visual perception. This paper reviews the recent research in content based video retrieval system. Also the paper focus on video structure analysis, like, frame extraction from video, key frame extraction, feature extraction using SURF, similarity measure, video indexing, and video browsing. This system retrieves similar videos based on local feature detector and descriptor called SURF (Speeded-Up Robust Feature). For image convolution SURF relies on integral images. In SURF we use Hessian matrix-based measure for the detector and a distribution-based descriptor. SURF can be computed and compared much faster with respect to repeatability, uniqueness and robustness. SURF is better than previous proposed methods as SIFT, PCA-SIFT, GLOH, etc. Finally the future scope in this system is specified.

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Content Based Video Retrieval Using SURF Descriptor

Cited by 17 publications

References 10 publications

Implementing Content Based Video Retrieval Using Speeded-Up Robust Features

Implementing Content Based Video Retrieval Using Speeded-Up Robust Features

Event-Oriented 3D Convolutional Features Selection and Hash Codes Generation Using PCA for Video Retrieval

Content based Video Retrieval using Enhance Feature Extraction

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