Accommodating hybrid retrieval in a comprehensive video database management system

Chan, Shan-Bin; Li, Qing; Wu, Yi; Zhuang, Yueting

doi:10.1109/tmm.2002.1017730

Cited by 16 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The most common optimization techniques are as follows: Content-Based Retrieval (CBR) CBR technique is the communistic one. The queries are used to access semantic information included in multimedia data like video data and motion information [6]. A retrieval process takes advantages of texture, shape, color, and other properties from raw data to analyze this data in respect meaningful entities and geometric patterns [7].…”

Section: Query Optimization Techniquesmentioning

confidence: 99%

Comparison of query optimization techniques in content based image retrieval (CBIR)

Alharthi¹,

Alqurashi²

2019

IJAMR

View full text Add to dashboard Cite

With increasing the popularity of World Wide Web, storing digital contents increases enormously, in that case, it is important to implement convenient information systems which manage the collections of these digital contents efficiently. This paper concentrates on hastening techniques for efficient retrieval of images. Content-Based Image Retrieval (CBIR) systems are used by common approaches. These systems support retrieving similar images depend on content properties (e.g., color, shape, and texture) by retrieving automatically similar images to a pattern or user-defined specification. The CBIR generally used in several applications by applying different techniques in each application which in turns enhance the retrieval process. The paper aims to evaluate some of these applications and compare them to find out the proper methods that return the best results in these CBIR systems.

show abstract

Section: Query Optimization Techniquesmentioning

confidence: 99%

Comparison of query optimization techniques in content based image retrieval (CBIR)

Alharthi¹,

Alqurashi²

2019

IJAMR

View full text Add to dashboard Cite

show abstract

“…Feature vectors are extracted by image preprocessing and metadata information, such as keywords, semantic information, and visual information, which are manually or automatically added [10,11].…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Web Digital Image Metadata Service Platform for Social Curation Commerce Environment

Hong

Lee

2015

Modelling and Simulation in Engineering

View full text Add to dashboard Cite

Information management includes multimedia data management, knowledge management, collaboration, and agents, all of which are supporting technologies for XML. XML technologies have an impact on multimedia databases as well as collaborative technologies and knowledge management. That is, e-commerce documents are encoded in XML and are gaining much popularity for business-to-business or business-to-consumer transactions. Recently, the internet sites, such as e-commerce sites and shopping mall sites, deal with a lot of image and multimedia information. This paper proposes an intelligent web digital image information retrieval platform, which adopts XML technology for social curation commerce environment. To support object-based content retrieval on product catalog images containing multiple objects, we describe multilevel metadata structures representing the local features, global features, and semantics of image data. To enable semantic-based and content-based retrieval on such image data, we design an XML-Schema for the proposed metadata. We also describe how to automatically transform the retrieval results into the forms suitable for the various user environments, such as web browser or mobile device, using XSLT. The proposed scheme can be utilized to enable efficient e-catalog metadata sharing between systems, and it will contribute to the improvement of the retrieval correctness and the user's satisfaction on semantic-based web digital image information retrieval.

show abstract

“…They have also not provided the queries that can help in retrieval of information, since they target video data mining. In [22], a video management and application processing framework (VideoMap) is proposed. The main component of VideoMap is query-based video retrieval mechanism.…”

Section: Related Workmentioning

confidence: 99%

Spatio-Temporal Querying of Video Content Using SQL for Quantizable Video Databases

Jain¹,

Aygün²

2009

JMM

View full text Add to dashboard Cite

Multimedia database modeling and representation play an important role for efficient storage and retrieval of multimedia. Modeling of semantic video content that enables spatio-temporal queries is one of the challenging tasks. A video is called as “quantizable” if the instants of a video are enough for a person to imagine the missing scenes properly. A semantic query for quantizable videos can be defined in a more flexible way using spatio-temporal instants. In this paper, we provide a semantic modeling and retrieval system, termed as G-SMART. Firstly, the videos are quantized according to semantic events. Then semantic instants and events of the video that include objects, events, and locations are provided as a grammar-based string. This linear string representation enables both the spatial and temporal retrieval of the video using Structured Query Language (SQL). The redundancy in this linear representation is reduced by using data reduction properties such as removal of implied information. Various types of queries such as event-object-location, event- location, object-location, event-object, current-next event, projection and semantic event are supported by G-SMART. A graphical user interface is designed to build queries and view the query results. GSMART enables multimodal presentation by displaying the query results in the form images and videos. We show our results on a tennis video database.

show abstract

Accommodating hybrid retrieval in a comprehensive video database management system

Cited by 16 publications

References 29 publications

Comparison of query optimization techniques in content based image retrieval (CBIR)

Comparison of query optimization techniques in content based image retrieval (CBIR)

An Intelligent Web Digital Image Metadata Service Platform for Social Curation Commerce Environment

Spatio-Temporal Querying of Video Content Using SQL for Quantizable Video Databases

Contact Info

Product

Resources

About