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Architecture for Software as a Service (SaaS) Model of CBIR on Hybrid Cloud of Microsoft Azure 7

Architecture for Software as a Service (SaaS) Model of CBIR ...

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7th International conference on Communication, Computing and virtualization (ICCCv)

作者： Meena, Mamta Singh, Anamika Ram Bharadi, vinayak A. Shri Jagdishprasad Jharmal Tibrewala Univ Jhunjhunu 333001 India Thakur Coll Engn & Technol Bombay 400101 Maharashtra India

In various areas of government, academia, hospitals and commerce large collections of digital images are produced. Many of these collections are the merchandise of digitizing existing collections of analogue drawings, photographs, paintings and prints. Generally the only way of searching these collections was by basically browsing or keyword indexing. Digital images databases however, open the way to content-based searching. Content Based image retrieval (CBIR) is concerned with the retrieval of images similar to a specified image, from an image repository. Content Based image retrieval (CBIR) is an efficient retrieval of relevant images from large databases based on features extracted from the image. This paper proposes a system that can be used for retrieving images related to a query image from a large set of distinct images. It follows an image segmentation based approach to extract the different features present in an image. The above features which can be stored in vectors called feature vectors and therefore these are compared to the feature vectors of query image and the image information is sorted in decreasing order of similarity. The processing of the same is done on cloud. The CBIR system is an application built on Windows Azure platform. It is a parallel processing problem where a large set of images have to be operated upon to rank them based on a similarity to a provided query image by the user. Numerous instances of the algorithm run on the virtual machines provided in the Microsoft data centers, which run Windows Azure. Windows Azure Stack is the operating system for the cloud by Microsoft Incorporation. Windows azure Stack is responsible for creating ideal hybrid architecture (C) 2016 The Authors. Published by Elsevier B.v. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Organizing Committee of ICCCv 2016

关键词： Content Based image retrieval (CBIR) Web Role Blob storage Azure Stack Introduction

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3rd International conference on visual Information and Information Systems, vISUAL 1999

3rd International Conference on Visual Information and Infor...

引用

3rd International conference on visual Information Systems, vISUAL 1999

ISBN: (纸本)3540660798

The proceedings contain 101 papers. The special focus in this conference is on visual Information Systems, Interactive visual Query, Browsing Information Space, Internet Search Engines and video Parsing. The topics include: Supporting image-retrieval by database driven interactive 3d information-visualization;querying multimedia data sources and databases;system for medical image retrieval;error-tolerant database for structured images;query processing and optimization for pictorial query trees;similarity search using multiple examples in mars;excluding specified colors from image queries using a multidimensional query space;generic viewer interaction semantics for dynamic virtual video synthesis;category oriented analysis for visual data mining;user interaction in region-based color image segmentation;using a relevance feedback mechanism to improve content-based image retrieval;region queries without segmentation for image retrieval by content;content-based image retrieval over the web using query by sketch and relevance feedback;task analysis for information visualization;exploiting interaction in image retrieval;visualization of information spaces to retrieve and browse image data;an architecture for using images to access and organize web information;a compact and retrieval-oriented video representation using mosaics;a visual search engine for distributed image and video database retrieval applications;a dynamic java-based intelligent interface for online image database searches;motion-based feature extraction and ascendant hierarchical classification for video indexing and retrieval;automatically segmenting movies into logical story units and scene segmentation and image feature extraction for video indexing and retrieval.

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Scene change detection and feature extraction for MPEG-4 sequences

Scene change detection and feature extraction for MPEG-4 seq...

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7th storage and retrieval for image and video databases conference

作者： Divakaran, A Ito, H Sun, HF Poon, T Mitsubishi Elect Informat Technol Amer Adv Televis Lab New Providence NJ 07974 USA

ISBN: (纸本)0819431273

In this paper we present a new computationally efficient and effective technique for detection of abrupt scene changes in MPEG-4/2 compressed video sequences. We combine the de image based approach of Yeo and Liu(1) with the bit allocation change based approach of Feng, Lo and Mehrpour.(2) The bit allocation based approach has the advantage of computational simplicity since it only requires entropy decoding of the sequence. Since extraction of de images from I-Frames/Objects is simple, the de image based technique of Yeo is a good alternative for comparison of I-frames/objects. For P-frames/objects however, Yeo's algorithm requires additional computation. We find that the bit allocation change based approach is prone to false detection in comparison of intra-coded objects in MPEG-4 sequences. However, if a suspected scene/object change has been located accurately in a group of consecutive frames/objects, the bit allocation based technique quickly and accurately locates the cut point therein. This motivates us to use de image based detection between successive I-Frames/Objects to identify the sub-sequences with scene/object changes, and then use bit allocation based detection to find the cut point therein. Our technique thus has only a marginally greater complexity than the completely bit allocation based technique but has greater accuracy. It is applicable to both MPEG-2 sequences and MPEG-4 multiple-object sequences. In the MPEG-4 multiple object case, we use a weighted sum of the change in each object of the frame using the area of the object as the weight.

关键词： compressed-domain video processing video segmentation MPEG-4 MPEG-2 scene change detection

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Model-based classification of visual information for content-based retrieval

Model-based classification of visual information for content...

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

作者： Jaimes, Alejandro Chang, Shih-Fu Columbia Univ New York NY United States

A different approach to content-based retrieval and a novel framework for classification of visual information are proposed. The visual Apprentice which is an implementation of the framework for still images and video that uses a combination of lazy-learning, decision trees, and evolution programs for classification and grouping is introduced. Examples and results are given to demonstrate the applicability of the proposed approach to perform visual classification and detection.

关键词： Feature extraction

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video and image clustering using relative entropy

Video and image clustering using relative entropy

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

作者： Iyengar, Giridharan Lippman, Andrew B. MIT Media Lab Cambridge MA United States

In this paper, we present an approach to clustering video sequences and images for efficient retrieval using relative entropy as our cost criterion. In addition, our experiments indicate that relative entropy is a good similarity measure for content-based retrieval. In our clustering work, we treat images and video as probability density functions over the extracted features. This leads us to formulate a general algorithm for clustering densities. In this context, it can be seen that an euclidean distance between features and the Kullback-Liebler (KL) divergence give equivalent clustering. In addition, the asymmetry of the KL divergence leads to another clustering. Our experiments indicate that this clustering is more robust to noise and distortions compared with the one resulting from euclidean norm.

关键词： image segmentation

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Refining image retrieval based on context-driven methods

Refining image retrieval based on context-driven methods

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

作者： Hong, Dezhong Wu, Jian-Kang Singh, Sumeet Sohan RWCP Information-Base Functions KRDL Lab Singapore

A prototype of the content-based image retrieval system is implemented based on the algorithms introduced in this paper. The image contents at the high levels are extracted. The fuzzy C-means classifier is employed to compute the object clusters and provide useful information for overlapped clusters. The automatic image segmentation and categorisation is achieved. To obtain the context for image retrieval, the subjective context and the objective context are modelled by means of the fuzzy sets theory. The system is able to trace the users' interactions during retrieval. The refinements of the retrieval results can be made while the users are submitting the queries telling the specific requirements.

关键词： Feature extraction

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image query and indexing for digital X-rays

Image query and indexing for digital X-rays

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

作者： Long, L.Rodney Thoma, George R. Natl Library of Medicine Bethesda MD United States

The Web-based Medical Information retrieval System (WebMIRS) allows Internet access to databases containing 17,000 digitized x-ray spine images and associated text data from National Health and Nutrition Examinations Surveys (NHANES). WebMIRS allows SQL query of the text, and viewing of the returned text records and images using a standard browser. We are now working (1) to determine utility of data directly derived from the images in our databases and (2) to investigate the feasibility of computer-assisted or automated indexing of the images to support image retrieval of images of interest to biomedical researchers in the field of osteoarthritis. To build an initial database based on image data, we are manually segmenting a subset of the vertebrae, using techniques from vertebral morphometry. From this, we will derive and add to the database vertebral features. This image-derived data will enhance the user's data access capability by enabling the creation of combined SQL/image-content queries.

关键词： Information retrieval systems

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Audio characterization for video indexing

Audio characterization for video indexing

引用

storage and retrieval for Still image and video databases Iv

作者： Patel, Nilesh v. Sethi, Ishwar K. Wayne State Univ. Detroit MI USA

ISBN: (纸本)0819420441

The major problem facing video databases is that of content characterization of video clips once the cut boundaries have been determined. The current efforts in this direction are focussed exclusively on the use of pictorial information, thereby neglecting an important supplementary source of content information, i.e. the embedded audio or sound track. The current research in audio processing can be readily applied to create many different video indices for use in video On Demand (vOD), educational video indexing, sports video characterization, etc. MPEG is an emerging video and audio compression standard with rapidly increasing popularity in multimedia industry. Compressed bit stream processing has gained good recognition among the researchers. We have also demonstrated feature extraction in MPEG compressed video which implements a majority of scene change detection schemes on compressed video. In this paper, we examine the potential of audio information for content characterization by demonstrating the extraction of widely used features in audio processing directly from compressed data stream and their application to video clip classification.

关键词： Indexing (of information)

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video retrieval by spatial and temporal structure of trajectories

Video retrieval by spatial and temporal structure of traject...

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conference on storage and retrieval for Media databases 2001

作者： Little, JJ Gu, Z Univ British Columbia Dept Comp Sci Vancouver BC V6T 1Z4 Canada

ISBN: (纸本)0819439932

Our goal is to enable queries about the motion of objects in a video sequence. Tracking objects in video is a difficult task, involving signal analysis, estimation and often semantic information particular to the targets. That is not our focus - rather, we assume that tracking is done, and turn to the task of representing the motion for query. The position over time of an object results in a motion trajectory, i.e., a sequence of locations. We propose a novel representation of trajectories: we use the path and speed curves as the motion representation. The path curve records the position of the object while the speed curve records the magnitude of its velocity. This separates positional information from temporal information, since position may be more important in specifying a trajectory than the actual velocity of a trajectory. velocity can be recovered from our representation. We derive a local geometric description of the curves invariant under scaling and rigid motion. We adopt a warping method in matching so that it is robust to variation in feature vectors. We show that R-trees can be used to index the multidimensional features so that search will be efficient and scalable to a large database.

关键词： video indexing trajectory curve representation motion representation

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MUvIS: a system for content-based indexing and retrieval in large image databases

MUVIS: a system for content-based indexing and retrieval in ...

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

作者： Cheikh, Faouzi Alaya Cramariuc, Bogdan Reynaud, Carole Quinghong, Meng Dragos-Adrian, Badea Hnich, Brahim Gabbouj, Moncef Kerminen, Petteri Makinen, Timo Jaakkola, Hannu Signal Processing Lab of Information Technology Tampere Finland

A new system, the so-called MUvIS, is introduced for content-based indexing and retrieval for image database management systems. In addition to traditional indexing by key words, MUvIS allows indexing of objects and images based on color, texture, shape and objects layout inside them. Due to the use of large vector features, the pyramid trees are employed to create the index structure.

关键词： Information retrieval systems

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