BilVideo Video Database Management System

Ulusoy, Özgür; Güdükbay, Uğur; Dönderler, Mehmet Emin; Şaykol, Ediz; Alper, Cemil

doi:10.1016/b978-012088469-8/50150-9

Cited by 5 publications

(6 citation statements)

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“…STQL (Spatio-Temporal Query Language) [36] tries to address this by integrating spatio-temporal data into existing data models as abstract data type. Recently, a rule-based spatio-temporal query language is proposed in BilVideo [38] which is a web-based VDBMS. BilVideo focuses on the task of spatio-temporal query processing and SQL-like query language.…”

Section: Video Query Processingmentioning

confidence: 99%

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A Graph-Based Approach for Modeling and Indexing Video Data

Lee

2006

Eighth IEEE International Symposium on Multimedia (ISM'06)

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Section: Video Query Processingmentioning

confidence: 99%

“…The final goal of video database management is how to retrieve video data efficiently and effectively. To do this, there have been many research work for video indexing [28,29,30,31,32] and query processing [33,34,35,36,37,38,39,40].…”

Section: Organizationmentioning

confidence: 99%

A Graph-Based Approach for Modeling and Indexing Video Data

Lee

2006

Eighth IEEE International Symposium on Multimedia (ISM'06)

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“…Many video query languages have been proposed, which can be divided into three groups as follows: (1) entirely new and specialized languages 6,7 which are developed to support domain specific applications and data models, (2) SQL-like query languages [8][9][10][11] which are based on some extensions of SQL, and (3) OQL-like query languages 12,13 which are based on some extensions of OQL. Table 1 shows the summary of various query languages mentioned above.…”

Section: Introductionmentioning

confidence: 99%

<title>STRG-QL: spatio-temporal region graph query language for video databases</title>

Lee

Çelebi

2008

SPIE Proceedings

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In this paper, we present a new graph-based query language and its query processing for a Graph-based Video Database Management System (GVDBMS). Although extensive researches have proposed various query languages for video databases, most of them have the limitation in handling general-purpose video queries. Each method can handle specific data model, query type or application. In order to develop a general-purpose video query language, we first produce Spatio-Temporal Region Graph (STRG) for each video, which represents spatial and temporal information of video objects. An STRG data model is generated from the STRG by exploiting object-oriented model. Based on the STRG data model, we propose a new graph-based query language named STRG-QL, which supports various types of video query. To process the proposed STRG-QL, we introduce a rule-based query optimization that considers the characteristics of video data, i.e., the hierarchical correlations among video segments. The results of our extensive experimental study show that the proposed STRG-QL is promising in terms of accuracy and cost.

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“…It is usually decomposed into some sub-queries which in fact can be regarded a combination of the low-level query and the spatialtemporal query [1] [83]. With these query types, many systems have been proposed in the past, such as OVID [77], QBIC [33], AVIS [1], CVQL [57] and BilVideo [103]. We notice that some of them also design index structure for the enhanced query type.…”

Section: Related Workmentioning

confidence: 99%

“…It also designs indices to accelerate the query processing. BilVideo is proposed in [103]. It has three modules, namely, fact-extractor, videoannotator and object extractor, for assisting users to extract the information.…”

Section: Frame-level Filteringmentioning

confidence: 99%

Multi-level Video Filtering Using Non-textual Contents

Du¹

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Video is one of the major media human uses to store information. As the recording and storing devices become cheaper, there are numerous videos generated nowadays. The unprecedentedly large volume creates considerable new requirements on accessing the videos. Therefore, how to perform video filtering, i.e. obtaining a set of relevant video clips from the video repository becomes a challenging research topic. In previous works, video filtering required user entering some texts to filter the irrelevant video clips, which made the video filtering methods same as the document filtering methods for a long time. However, there are three limitations of the text-based video filtering: (1) it dismisses the rich contents in the videos; (2) it is inapplicable when the texts are absent, incomplete or sparse; (3) it fails to support in-video filtering. These limitations make the text-based video filtering powerless after the new requirements emerge. In recent years, there sees a tendency that computer could parse more meaningful contents from the videos. These non-textual contents are complementary to the texts in many cases. Enlightened by that, existing video filtering research gradually shifts from text-based to non-textual-based. Under this direction, we study how to improve the video filtering systematically from three levels.Frame-level. We propose to use detected visual object to filter the videos. In previous works, the visual objects were obtained manually where human took the responsibility of identifying the visual objects and connecting them in the videos. The process of obtaining the visual objects is costly when the data keep changing. Therefore, we proposed to leverage the object detection to obtain the visual objects automatically for frame-level filtering. However, object detection itself is unable to identify and connect the visual objects like human. To achieve that, we proposed a hybrid method to identify and connect the visual objects, which is further divided into local merge, propagation and global merge. We examined the proposed method on a real-world dataset then studied two issues:(1) whether the identifications and connections were accurate, as well as (2) how the environment influenced the proposed method. The experimental results were promising and proved that using detected visual objects for frame-level filtering is feasible.Video-level. We discover a new small content set for surveillance video filtering. Surveillance video filtering, namely surveillance event detection (SED), is important for many safety and security applications. It aims to alarm the events from the surveillance videos. Different from classical video filtering which extracts video content vectors from diverse sources, SED is only able to leverage the motion contents. And the state-of-the-art content set for surveillance is made up of STIP and MoSIFT.iii In our study, we proposed a new content set by using dense trajectory (DT) and improved dense trajectory (IDT). According to our analysis, our new content set capture...

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BilVideo Video Database Management System

Cited by 5 publications

References 0 publications

A Graph-Based Approach for Modeling and Indexing Video Data

A Graph-Based Approach for Modeling and Indexing Video Data

<title>STRG-QL: spatio-temporal region graph query language for video databases</title>

Multi-level Video Filtering Using Non-textual Contents

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