The Experts below are selected from a list of 4308 Experts worldwide ranked by ideXlab platform

Dayuan Xue - One of the best experts on this subject based on the ideXlab platform.

  • China National Biodiversity Information Query System
    Journal of Environmental Management, 1999
    Co-Authors: Zhenning Gao, Dayuan Xue
    Abstract:

    There is no mechanism for the exchange of biodiversity information in China. It is therefore necessary to establish a system for providing metadata to help people to find data and to understand its content and characteristics. The China National Biodiversity Information Query System (CNBIQS) is just such a metadatabase. Systems analysis, systems design and systems implementation for CNBIQS are discussed in this paper. CNBIQS includes metadata on institutions, data sets, databases, books and maps related to biodiversity. China is rich in information and the system reflects the present status of biodiversity information in the country. It possesses powerful functions for information search, such as Query by institute Category, Query by thematic information Category, Query by title and Query by author. ” 1999 Academic Press

Evelyn Brindha - One of the best experts on this subject based on the ideXlab platform.

  • Image Similarity Learning through Rankboost Mechanism Based On Keywords and Content Queries
    2013
    Co-Authors: Veena Sharanya, Evelyn Brindha
    Abstract:

    Different types of search engines are used to search image and text contents. Two types of image search methods are available in the Internet. They are Query keyword based model and content based image retrieval models. Text Query strings are used in the textual image retrieval model. Content based image retrieval (CBIR) model uses the visual information of the images. Image search methods use the text annotation and image visual features. Google image search and Bing image search engines are used to fetch images from the web. Image Query string is used to search image on Internet. One click Query image selection method is used to submit user intention for image retrieval. Content based image re-ranking is performed with visual and textual similarity metrics. Adaptive Weight Schema is used for the similarity analysis. Feature weight learning algorithm is applied to estimate feature weights for the images and its Category. Query is expanded with keyword and visual information. Rank boost framework algorithm is enhanced to rank images with photographic quality. Content similarity and visual quality factors are used for the re-ranking process. In this paper, we propose an image indexing and retrieval using speech annotations based on a predefined structured syntax. To improve the retrieval effectively, N-best lists for index generation is used .so, a Query expansion technique is explored to enhance the Query terms. All this process is automatic, without extra effort from the user. This is critically important in web-based image search engine for any commercial, where the user interface has to be extremely simple.

Zhenning Gao - One of the best experts on this subject based on the ideXlab platform.

  • China National Biodiversity Information Query System
    Journal of Environmental Management, 1999
    Co-Authors: Zhenning Gao, Dayuan Xue
    Abstract:

    There is no mechanism for the exchange of biodiversity information in China. It is therefore necessary to establish a system for providing metadata to help people to find data and to understand its content and characteristics. The China National Biodiversity Information Query System (CNBIQS) is just such a metadatabase. Systems analysis, systems design and systems implementation for CNBIQS are discussed in this paper. CNBIQS includes metadata on institutions, data sets, databases, books and maps related to biodiversity. China is rich in information and the system reflects the present status of biodiversity information in the country. It possesses powerful functions for information search, such as Query by institute Category, Query by thematic information Category, Query by title and Query by author. ” 1999 Academic Press

Veena Sharanya - One of the best experts on this subject based on the ideXlab platform.

  • Image Similarity Learning through Rankboost Mechanism Based On Keywords and Content Queries
    2013
    Co-Authors: Veena Sharanya, Evelyn Brindha
    Abstract:

    Different types of search engines are used to search image and text contents. Two types of image search methods are available in the Internet. They are Query keyword based model and content based image retrieval models. Text Query strings are used in the textual image retrieval model. Content based image retrieval (CBIR) model uses the visual information of the images. Image search methods use the text annotation and image visual features. Google image search and Bing image search engines are used to fetch images from the web. Image Query string is used to search image on Internet. One click Query image selection method is used to submit user intention for image retrieval. Content based image re-ranking is performed with visual and textual similarity metrics. Adaptive Weight Schema is used for the similarity analysis. Feature weight learning algorithm is applied to estimate feature weights for the images and its Category. Query is expanded with keyword and visual information. Rank boost framework algorithm is enhanced to rank images with photographic quality. Content similarity and visual quality factors are used for the re-ranking process. In this paper, we propose an image indexing and retrieval using speech annotations based on a predefined structured syntax. To improve the retrieval effectively, N-best lists for index generation is used .so, a Query expansion technique is explored to enhance the Query terms. All this process is automatic, without extra effort from the user. This is critically important in web-based image search engine for any commercial, where the user interface has to be extremely simple.

You Feng - One of the best experts on this subject based on the ideXlab platform.

  • The Concept,Content and System of Genetic Resources Metadatabase on China Marine Economically Valuable (Cultivated) Animals
    2006
    Co-Authors: You Feng
    Abstract:

    Study on the protection and conservation of marine biological genetic resources has gained much widerly with the development of marine aquaculture in China.The more and more data have been collected but how to share these data demands attention too.Sharing of nature resoures and biodiversity information useful for aquaculture and related research is the key to their sustainable utilization.Metadata refers to information on the location,source,content,or other specifies in relation to actual data.Metadatabase is a database designed and developed for metadata conservation and management, which provides services for the management,Query and use of actual data.This system has powerful information Query functions, such as Query by institute catalogue,Query by thematic information Category,Query by name,and Query by.It is rich in information.There are some metadatabases such as China Information Network for Sustainable Development and other links on the Internet.But,there is no mechanism for marine economically valuable (cultivated) animal genetic resources information exchanging and sharing in China at present.It is therefore necessary and urgent to set up an information Query channel with metadata to help people know where to find required information on marine cultivated species and how to use them.This paper clarifies the definition, role and meaning of metadata,and outlined the concept, key elements and standards of metadatabase.The general status of biodiversity metadatabase in our country is also reviewed.We discussed the contents and strategies to set up the China Marine Economically Valuable (Cultivated) animal genetic resources metadatabase in the present paper, including standing criterion institute on collecting, processing, metadatabase constituting and sharing of information sharing of scientific data on genetic resources, and data collection framework designing.The conceptical basis on how to organize and construct this kind of database is presented.