Ontology-Supported Video Modeling and Retrieval

Yıldırım, Yakup; Yazıcı, Adnan

doi:10.1007/978-3-540-71545-0_3

Cited by 7 publications

(6 citation statements)

References 12 publications

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“…It is implemented in Matlab & Portege [29] as ontology development environment. [19,20] The first case is where target is to extract the exact event footage from the cricket match video. There are many cricket match recorded videos available on internet which is being used for study purpose.…”

Section: Test Resultsmentioning

confidence: 99%

“…We proposed variant semantic video content extraction system which contributes to video event modeling and thus video content analysis area. This is achievable upto certain degree by specifically focusing on segmentation [19,20,21] & classification aspect of video retrieval process [5] This is often accomplished through the event finding based on ontology and content extraction algorithms [13,14,15].It is variants of linguistics content extraction system that permits the user to question and retrieve objects, events, and concepts that are measured & extracted Automatically [5] The block diagram of video event extraction system based on prominent object identifier and Spatio-temporal inter objects relation based event semantics is illustrated in Fig. 2 .Initially, we have used a meta-ontology, a rule construction norms that is domain specific to construct domain Ontology.…”

Section: Proposed Systemmentioning

confidence: 99%

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Video Events Extraction based on Mixed Feature Modeling

Daga¹,

Ghatol²,

Daga³

2017

IJCA

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Now a day"s increase in access of video-based application has opened need for extracting the content in videos. Unprocessed data and low-level features alone are not sufficient to complete the user"s need so deeper understanding of the content at the semantic level is required. Currently, manual techniques which are inefficient, subjective and expensive in time and limit the querying capabilities are used to fulfill the gap between low-level representative features and high-level semantic content. The system that allows the user to query and retrieve associated objects, events, and concepts automatically is proposed .The events can also be representative objects, actions, their impressions, and so on. Here an ontology-based video semantic content model which uses spatial/temporal relations in event and concept definitions is leveraged. Simple & efficient process consideration on main object detection & its common associated mixed direct measurable feature like shape, texture & derived features like co-occurrence & topology is evaluated. An ontology definition provides a wide-domain applicable rule construction standard. In addition to domain ontology"s, additional rule definitions to lower spatial relation computation cost are used. This leads to describe some complicated events close to human thinking. The proposed system has been implemented and tested on domains like road accident & sports for precision and recall measures.

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Section: Test Resultsmentioning

confidence: 99%

Section: Proposed Systemmentioning

confidence: 99%

Video Events Extraction based on Mixed Feature Modeling

Daga¹,

Ghatol²,

Daga³

2017

IJCA

View full text Add to dashboard Cite

show abstract

“…Yet, it requires excessive training and large training samples and special in only flower videos with just 30 flower classes. In [15], Asha, et al, introduced an online video URL query approach with multiple features technique using color distributions, texture & motion, and binary patterns feature vector with no indexing criteria and a Euclidean distance retrieval algorithm and acceptable performance on a small scale experimental dataset of 40 videos in 4 categories. Table 3 shows the comparison between all mentioned techniques against the proposed technique in this work in terms of precision, recall, and F1 measure performances.…”

Section: Related Workmentioning

confidence: 99%

Performance Comparisons of Content-Based Video Search Engine Retrieval Using Different Similarity Algorithms

Adly

Hegazy

Elarif

et al. 2023

Preprint

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Content-based video search engines (CBVSE) are broadly needed in many mainstream video search engines retrieving videos from public video streaming services over the Internet such as YouTube. As they are mostly text-based search engines that index and retrieve videos depending on the surrounding text around the video web page that contains information representing this video file. This paper is an attempt to improve the performance of a previously developed technique for a content-based video search engine designed to firstly index videos on YouTube and search and retrieve videos using a non-semantic video query. Moreover, a large-scale dataset was indexed containing more than 1088 YouTube video records. Each record contains a feature vector of the temporal set, key-objects sets, and keyframes representing video shots in each video file in addition to the URLs and other information gathered from each video file web page.

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“…It is formal language for representing events for describing ontology for application domain and for annotating data with those ontology categories. To address the problem of designing ontology for visual activity recognition [16] proposes a system. On general ontology design principles and adapt them to the specific domain of human activity ontology.…”

Section: Literature Surveymentioning

confidence: 99%

Fuzzy Ontology and Rule based Model for Automatic Semantic Content Extraction from Videos using k-Means Algorithm

Nikam¹,

Nandwalkar²

2015

IJCA

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Video based applications disclosed need for efficiently extracting and modeling the video contents. The video features can be classified into normal data, relative features and logic content. Semantic level understanding is required for core content of video. So to get video content automatic semantic content framework is proposed. In proposed system a semantic content extraction system that allows the user to query and regain objects, events, and concepts that are extracted automatically. VISCOM is a video semantic content model which contains classes and relations between classes. Objects and events are represented by some VISCOM classes and other classes are used in the automatic semantic content extraction framework. VISCOM classes collect the semantic content types and relations.Ontology based fuzzy video data semantic model which uses spatial and temporal relations in event and concept definition is proposed. Extracted objects from consecutive representative frames are processed to extract temporal relations. Additional rules to lower spatial relation computation cost and to define some difficult situations more successfully are used. To extract objects from video we apply k-means clustering algorithm. By, which we get the more relevant objects related to user query.

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Ontology-Supported Video Modeling and Retrieval

Cited by 7 publications

References 12 publications

Video Events Extraction based on Mixed Feature Modeling

Video Events Extraction based on Mixed Feature Modeling

Performance Comparisons of Content-Based Video Search Engine Retrieval Using Different Similarity Algorithms

Fuzzy Ontology and Rule based Model for Automatic Semantic Content Extraction from Videos using k-Means Algorithm

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