Image Retrieval

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Wang Huifeng - One of the best experts on this subject based on the ideXlab platform.

  • A Summary of Semantics-based Image Retrieval
    Computer Engineering, 2001
    Co-Authors: Wang Huifeng
    Abstract:

    In Image Retrieval system today, there exists a significant gap between the user's information needs and what the systems can deliver. To increase the Image Retrieval system's ability, we require a proper semantic description for Image content. In this article, we give a comprehensive discuss about multi-level semantic modeling and knowledge representation in Image Retrieval, especially about the methods with which to mapping visual features to high lever Image semantic contents. To make Image Retrieval using semantics efficiently and effectively, we point out the research progress we need to make at present and future.

Jin Hou - One of the best experts on this subject based on the ideXlab platform.

  • DICTA - Semantic Image Retrieval Using Region Based Inverted File
    2009 Digital Image Computing: Techniques and Applications, 2009
    Co-Authors: Dengsheng Zhang, Monirul Islam, Jin Hou
    Abstract:

    Image data is as common as textual data in this digital world. There is an urgent demand of Image management tools as efficient as those text search engines. Decades of research on Image Retrieval has found there is a significant gap between the existing content based Image Retrieval and semantic interpretation of Images by human. As a result, recent research on Image Retrieval has shifted to semantic Image Retrieval. Many semantic Image Retrieval models have been proposed, however, these methods are still alienated from the widely accepted text based Retrieval method. In this paper, we propose to unite the semantic Image Retrieval model with text based Retrieval using a novel region based inverted file indexing method. For this purpose, Images are translated into textual documents which are then indexed and retrieved the same way as the conventional text based search. Results show that our method not only provides text based search efficiency, but also better performance than the conventional low level Image Retrieval.

Yuan Zhenming - One of the best experts on this subject based on the ideXlab platform.

  • Image Retrieval Based on Web
    Computer Engineering, 2002
    Co-Authors: Yuan Zhenming
    Abstract:

    This paper introduces a technology of Image Retrieval based on Web. It brings forward an Image Retrieval model and describes the relationship between Image content and the semantic of surrounding text in webpages. In this paper, the calculation of similarity, word match algorithm, feedback of Retrieval technology are discussed in detail. Moreover, experimental study shows that the Retrieval model is effective.

Dengsheng Zhang - One of the best experts on this subject based on the ideXlab platform.

  • DICTA - Semantic Image Retrieval Using Region Based Inverted File
    2009 Digital Image Computing: Techniques and Applications, 2009
    Co-Authors: Dengsheng Zhang, Monirul Islam, Jin Hou
    Abstract:

    Image data is as common as textual data in this digital world. There is an urgent demand of Image management tools as efficient as those text search engines. Decades of research on Image Retrieval has found there is a significant gap between the existing content based Image Retrieval and semantic interpretation of Images by human. As a result, recent research on Image Retrieval has shifted to semantic Image Retrieval. Many semantic Image Retrieval models have been proposed, however, these methods are still alienated from the widely accepted text based Retrieval method. In this paper, we propose to unite the semantic Image Retrieval model with text based Retrieval using a novel region based inverted file indexing method. For this purpose, Images are translated into textual documents which are then indexed and retrieved the same way as the conventional text based search. Results show that our method not only provides text based search efficiency, but also better performance than the conventional low level Image Retrieval.

Vipin Tyagi - One of the best experts on this subject based on the ideXlab platform.

  • Content-Based Image Retrieval: An Introduction
    Content-Based Image Retrieval, 2017
    Co-Authors: Vipin Tyagi
    Abstract:

    This chapter provides an introduction to information Retrieval and Image Retrieval. Types of Image Retrieval techniques, i.e., text-based Image Retrieval and content-based Image Retrieval techniques are introduced. A brief introduction to visual features like color, texture, and shape is provided. Similarity measures used in content-based Image Retrieval and performance evaluation of content-based Image Retrieval techniques are also given. Importance of user interaction in Retrieval systems is also discussed.

  • Region-Based Image Retrieval
    Content-Based Image Retrieval, 2017
    Co-Authors: Vipin Tyagi
    Abstract:

    Content-based Image Retrieval involves extraction of global and region features for searching an Image from the database. This chapter provides an introduction to content-based Image Retrieval according to region-based similarity known as region-based Image Retrieval (RBIR). Regions of interest from an Image can be selected automatically by the system or can be specified by the user. It increases the accuracy of the Retrieval results as regions of interests are capable of reflecting user-specific interest with greater accuracy. However, success of automatic selection of region of interest-based methods largely depends on the segmentation technique used. In this chapter, state-of-the-art techniques for region-based Image Retrieval are discussed.

  • A Survey on Texture Image Retrieval
    Advances in Intelligent Systems and Computing, 2015
    Co-Authors: Ghanshyam Raghuwanshi, Vipin Tyagi
    Abstract:

    Retrieving Images from the large databases has always been one challenging problem in the area of Image Retrieval while maintaining the higher accuracy and lower computational time. Texture defines the roughness of a surface. For the last two decades due to the large extent of multimedia database, Image Retrieval has been a hot issue in Image processing. Texture Images are retrieved in a variety of ways. This paper presents a survey on various texture Image Retrieval methods. It provides a brief comparison of various texture Image Retrieval methods on the basis of Retrieval accuracy and computation time with the benchmark databases. Image Retrieval techniques vary with feature extraction methods and various distance measures. In this paper, we present a survey on various texture feature extraction methods by applying variants of wavelet transform. This survey paper facilitates the researchers with background of progress of Image Retrieval methods that will help researchers in the area to select the best method for texture Image Retrieval appropriate to their requirements.