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conference on Multimedia on Mobile Devices and Multimedia Content Access - Algorithms and Systems v

作者： Mallepudi, Sri Abhishikth Calix, Ricardo A. Knapp, Gerald M. Louisiana State Univ Baton Rouge LA 70803 USA

ISBN: (纸本)9780819484185

In recent years there has been a rapid increase in the size of video and image databases. Effective searching and retrieving of images from these databases is a significant current research area. In particular, there is a growing interest in query capabilities based on semantic image features such as objects, locations, and materials, known as content-based image retrieval. This study investigated mechanisms for identifying materials present in an image. These capabilities provide additional information impacting conditional probabilities about images (e. g. objects made of steel are more likely to be buildings). These capabilities are useful in Building Information Modeling (BIM) and in automatic enrichment of images. I2T methodologies are a way to enrich an image by generating text descriptions based on image analysis. In this work, a learning model is trained to detect certain materials in images. To train the model, an image dataset was constructed containing single material images of bricks, cloth, grass, sand, stones, and wood. For generalization purposes, an additional set of 50 images containing multiple materials (some not used in training) was constructed. Two different supervised learning classification models were investigated: a single multi-class SvM classifier, and multiple binary SvM classifiers (one per material). image features included Gabor filter parameters for texture, and color histogram data for RGB components. All classification accuracy scores using the SvM-based method were above 85%. The second model helped in gathering more information from the images since it assigned multiple classes to the images. A framework for the I2T methodology is presented.

关键词： image classification Binary data RGB color model Machine learning image filtering Data modeling Feature extraction databases Buildings Content based image retrieval

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Clustering declustered data for efficient retrieval 99

Clustering declustered data for efficient retrieval

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8th International conference on Information Knowledge Management (CIKM 99)

作者： Ferhatosmanoglu, H Agrawal, D El Abbadi, A Univ Calif Santa Barbara Dept Comp Sci Santa Barbara CA 93106 USA

ISBN: (纸本)1581131461

Modern databases increasingly integrate new kinds of information, such as multimedia information in the form of image, video, and audio data. Both the dimensionality and the amount of data that need to be processed is increasing rapidly, increasing the demand for the efficient retrieval of large amounts of multi-dimensional data. Declustering techniques for multi-disk architectures have been effectively used for storage. In this paper, we first establish that besides exploiting the parallelism, a careful organization of each disk must be considered for fast searching. We introduce the notion of page allocation and data space mapping which can be used to organize and retrieve multidimensional data. We develop these notions based on three different partitioning strategies: regular grid partitioning, concentric hypercubes and hyperpyramids.. We develop techniques that satisfy efficient retrieval by optimizing the number of buckets retrieved by the query, disk arm movement and I/O parallelism. We prove that concentric hypercube-based mapping satisfies the optimal clustering and optimal parallelism. We develop a technique based on hyperpyramid partitioning that reduces the number of buckets retrieved by the query and has efficient inter- and intra-disk organizations. We evaluate the performance of proposed techniques by comparing them with the current approaches. The new techniques lead to very significant improvement over the existing techniques, and result in fast retrieval of multi-dimensional data.

关键词： Information retrieval systems

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Designing a new storage technique for face images transmission using JPEG2000

Designing a new storage technique for face images transmissi...

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8th International conference Advanced Communication Technology, ICACT 2006

作者： Huang, Hsiu-Mei You, Geeng-Neng Syu, Fu-Song Liaw, Shu-Sheng Department of Management Science National Tai-Chung Institute of Technology 129 Saming Rd. Taichung 404 Taiwan Department of Multimedia Design National Tai-Chung Institute of Technology 129 Saming Rd. Taichung 404 Taiwan General Education Center China Medical University 91 Shiuesh Rd. Taichung 404 Taiwan

ISBN: (纸本)8955191294

Pictures and video sequences showing human faces are of high importance in content-based retrieval systems. During flourishing Internet time, more and more human face image databases are stored on the Internet. They provide a great number of human face images to access. Therefore, considering the storage space and the network bandwidth, how to access human face image more efficiently has become an important topic. The primary goal of our method is to minimize the number of bits required to preserve the crucial facial features without any loss of information. In this paper, we propose a new technique based on JPEG2000 for the storage and retrieval of the human face images. We combine lossy and lossless image compression of JPEG2000 to reach our goal. Aiming at the feature of human face image data, we will extract the facial features in the image as the most important information. The lossless image compression is used to retain the facial features of human face image. The background and the rest of the image are processed by lossy compression to further save the required storage space. The preliminary result has shown that our method can have higher compression ratio without loss of the crucial facial feature for low bandwidth communication applications, such as videophone and teleconferencing.

关键词： image communication systems

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CSvD: Approximate similarity searches in high dimensional spaces using clustering and singular value decomposition

CSVD: Approximate similarity searches in high dimensional sp...

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conference on Multimedia storage and Archiving Systems III

作者： Thomasian, A Castelli, v Li, CS IBM Corp Thomas J Watson Res Ctr Yorktown Heights NY 10598 USA

ISBN: (纸本)0819429880

Many data-intensive applications, such as content-based retrieval of images or video from multimedia database and similarity retrieval of patterns;in data mining, require the ability the efficiently performing similarity queries. Unfortunately, the performance of nearest neighbor (NN) algorithms, the basis for similarity search, quickly deteriorates with the number of dimensions. In this paper we propose a method called Clustering with Singular value Decomposition (CSvD), combining clustering and singular value decomposition (SvD) to reduce the number of index dimensions. With CSvD, points are grouped into clusters that are more amenable to dimensionality reduction than the original dataset, Experiments with texture vectors extracted from satellite images show that CSvD achieves significantly higher dimensionality reduction than SvD alone for the satellite fraction of total variance with preserved. Conversely, for thr same compression ratio CSvD results in an increase in preserved total variance with respect to SvD (e.g,, a 70% increase fear a. 20:1 compression ratio). Then, approximate NN queries are more efficiently processed, as quantified through experimental results.

关键词： databases Earth observing sensors Satellite imaging Satellites Data compression Data mining Principal component analysis Feature extraction Multimedia video

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Web-based Multimedia Research and Indexation for Big Data databases 3

Web-based Multimedia Research and Indexation for Big Data Da...

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3rd International conference of Cloud Computing Technologies and Applications (CloudTech)

作者： Belarbi, Mohammed Amin Mahmoudi, Said Belalem, Ghalem Mahmoudi, Sidi Ahmed Univ Mons Fac Engn 20 Pl Parc Mons Belgium Ahmed Ben Bella Univ Oran1 Fac Exact & Appl Sci Oran Algeria

ISBN: (纸本)9781538611159

Indexing images represents a necessary tool for various domains related to computer vision such as video surveillance and movements analysis. visual characteristics of image such as color, texture, shape are used to identify the content of these frames. The high increase of data (Big Data) makes us face a problem of curse of dimensionality. In this paper, we propose a cloud-based technique for images indexing images using PCA (Principal component analysis) and a binary tree. Our work consists of exploiting SIFT (Scale Invariant Feature Transform) and SURF (Speeded up Robust Features) descriptors as frames features, PCA as dimensionality reduction method of SIFT and SURF features and the binary tree representation of these images in order to accelerate the research phase and reduce the storage space. Our approach consists of developing a system based on three phases: indexing images with SIFT and SURF features, compression within the application of PCA and finally the image retrieval in the cloud.

关键词： Indexing images SIFT SURF PCA Cloud Big Data

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User interface framework for image searching

User interface framework for image searching

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Proceedings of the 1999 7th conference of the storage and retrieval for image and video databases vII

作者： Brewer Jr., Orlie T. Boeing Co Seattle WA United States

This paper describes an API for image searching. The attempt was to isolate the functionality of the GUI from the functionality of the image search engine. The GUI would then make calls to the image search API and could be used with any image search engine implementing that API. Also, different methods of specifying the initial search image are discussed as well as different methods of displaying the results, including the use of 3D using vRML.

关键词： Graphical user interfaces

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Event-based modeling and processing of digital media 04

Event-based modeling and processing of digital media

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1st International Workshop on Computer vision Meets databases, CvDB 2004

作者： Singh, Rahul Li, Zhao Kim, Pilho Pack, Derik Jain, Ramesh School of Electrical and Computer Engineering Georgia Institute of Technology United States College of Computing Georgia Institute of Technology United States

ISBN: (纸本)1581139179

Capture, processing, and assimilation of digital media-based information such as video, images, or audio requires a unified framework within which signal processing techniques and data modeling and retrieval approaches can act and interact. In this paper we present the rudiments of such a framework based on the notion of "events". This framework serves the dual roles of a conceptual data model as well as a prescriptive model that defines the requirements for appropriate signal processing. Amongst the key advantages of this framework, lies the fact that it fundamentally brings together the traditionally diverse disciplines of databases and (various areas of) digital signal processing. In addition to the conceptual event-based framework, we present a physical implementation of the event model. Our implementation specifically targets the problem of processing, storage, and querying of multimedia information related to indoor group- oriented activities such as meetings. Such multimedia information may comprise of video, image, audio, and text-based data. We use this application context to illustrate many of the practical challenges that are encountered in this area, our solutions to them, and the open problems that require research across databases, computer vision, audio processing, and multimedia. Copyright 2004 ACM.

关键词： Digital storage

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Data Ingestions as a Service (DIaaS): A Unified Interface for Heterogeneous Data Ingestion, Transformation, and Metadata Management for Data Lake

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IEEE ACCESS 2024年 12卷 156131-156145页

作者： Sreepathy, H. v. Rao, B. Dinesh Jaysubramanian, Mohan Kumar Rao, B. Deepak Manipal Acad Higher Educ Manipal Sch Informat Sci Manipal 576104 India MulticoreWare Frameworks & Tools Coimbatore 641049 India

Data ingestion tools are critical component of Data Lake. Existing data ingestion tools face challenges of handling large variety, formats, sources of data. There exists void for unified data ingestion interface to handle the above research problems. This study proposes an innovative and integrated framework for data ingestion in a data lake, addressing the challenges posed by heterogeneous data sources, formats, and metadata management. The framework comprises three novel modules: First Unified Data Integration Connectors (UDIC), which provide seamless connectivity and data retrieval capabilities from diverse sources including databases, data warehouses, file systems, cloud storage, and APIs;Second, Adaptive Data variety Transformation (ADvT), a module that intelligently handles the transformation and processing of structured, semi-structured, and unstructured data types, ensuring efficient ingestion into the data lake;and third, Intelligent Metadata Management (IMM), a module that captures, stores, and manages metadata associated with the ingested data, offering advanced search, discovery, and enrichment functionalities. Comparative study corroborates features offered by the service with existing data ingestion tools to evaluate the novelty and significance of the study. Performance validation shows varying ingestion latencies across different data types: approximately 148.1 microseconds per record for structured data, 234.2 microseconds per record for semi-structured data, 65.6 microseconds per kilobyte (KB) for video data, and 42.7 microseconds per KB for image data. These results underscore the importance of considering data structure and size in optimizing ingestion processes. Overall, this research aims to revolutionize data ingestion in data lake environments by providing a unified solution for handling diverse data sources, formats, and metadata management.

关键词： Big Data applications Metadata Soft sensors Real-time systems Organizations Java Internet of Things Relational databases File systems Data lakes Data ingestion data lake data sources data formats unified interface data ingestion service big data

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Combining indexing and learning in iterative refinement

Combining indexing and learning in iterative refinement

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Proceedings of the 1999 7th conference of the storage and retrieval for image and video databases vII

作者： Li, Chung-Sheng Castelli, vittorio Smith, John R. Bergman, Lawrence IBM T.J. Watson Research Cent Yorktown Heights NY United States

A simultaneous learning and indexing technique is proposed for efficient content-based retrieval of images that can be described by feature vectors. This technique builds a compact high-dimensional index while taking into account that the raw feature space needs to be adjusted for each new application. With this technique, much better efficiency can be achieved as compared to those techniques that do not make provisions for efficient indexing.

关键词： image enhancement

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A compact and retrieval-oriented video representation using mosaics 3rd

A compact and retrieval-oriented video representation using ...

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3rd International conference on visual Information Systems, vISUAL 1999

作者： Baldi, Gabriele Colombo, Carlo Del Bimbo, Alberto Università di Firenze Via Santa Marta 3 FirenzeI-50139 Italy

ISBN: (纸本)3540660798

Compact yet intuitive representations of digital videos are required to combine high quality storage with interactive video indexing and retrieval capabilities. The advent of video mosaicing has provided a natural way to obtain content-based video representations which are both retrieval-oriented and compression-efficient. In this paper, an algorithm for extracting a robust mosaic representation of video content from sparse interest image points is described. The representation, which is obtained via visual motion clustering and segmentation, features the geometric and kinematic description of all salient objects in the scene, being thus well suited for video browsing, indexing and retrieval by visual content. Results of experiments on several Tv sequences provide an insight into the main characteristics of the approach. © Springer-verlag Berlin Heidelberg 1999.

关键词： video recording

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