<div style="text-align:justify;"> Digital image collection as rapidly increased along with the development of computer network. Image retrieval system was developed purposely to provide an efficient to...<div style="text-align:justify;"> Digital image collection as rapidly increased along with the development of computer network. Image retrieval system was developed purposely to provide an efficient tool for a set of images from a collection of images in the database that matches the user’s requirements in similarity evaluations such as image content similarity, edge, and color similarity. Retrieving images based on the content which is color, texture, and shape is called content based image retrieval (CBIR). The content is actually the feature of an image and these features are extracted and used as the basis for a similarity check between images. The algorithms used to calculate the similarity between extracted features. There are two kinds of content based image retrieval which are general image retrieval and application specific image retrieval. For the general image retrieval, the goal of the query is to obtain images with the same object as the query. Such CBIR imitates web search engines for images rather than for text. For application specific, the purpose tries to match a query image to a collection of images of a specific type such as fingerprints image and x-ray. In this paper, the general architecture, various functional components, and techniques of CBIR system are discussed. CBIR techniques discussed in this paper are categorized as CBIR using color, CBIR using texture, and CBIR using shape features. This paper also describe about the comparison study about color features, texture features, shape features, and combined features (hybrid techniques) in terms of several parameters. The parameters are precision, recall and response time. </div>展开更多
In medical research and clinical diagnosis, automated or computer-assisted classification and retrieval methods are highly desirable to offset the high cost of manual classification and manipulation by medical experts...In medical research and clinical diagnosis, automated or computer-assisted classification and retrieval methods are highly desirable to offset the high cost of manual classification and manipulation by medical experts. To facilitate the decision-making in the health-care and the related areas, in this paper, a two-step content-based medical image retrieval algorithm is proposed. Firstly, in the preprocessing step, the image segmentation is performed to distinguish image objects, and on the basis of the ...展开更多
In recent years there has been a significant interest in peer-to-peer （P2P） environments in the community of data management. However, almost all work, so far, is focused on exact query processing in current P2P dat...In recent years there has been a significant interest in peer-to-peer （P2P） environments in the community of data management. However, almost all work, so far, is focused on exact query processing in current P2P data systems. The autonomy of peers also is not considered enough. In addition, the system cost is very high because the information publishing method of shared data is based on each document instead of document set. In this paper, abstract indices （AbIx） are presented to implement content-based approximate queries in centralized, distributed and structured P2P data systems. It can be used to search as few peers as possible but get as many returns satisfying users＇ queries as possible on the guarantee of high autonomy of peers. Also, abstract indices have low system cost, can improve the query processing speed, and support very frequent updates and the set information publishing method. In order to verify the effectiveness of abstract indices, a simulator of 10,000 peers, over 3 million documents is made, and several metrics are proposed. The experimental results show that abstract indices work well in various P2P data systems.展开更多
文摘<div style="text-align:justify;"> Digital image collection as rapidly increased along with the development of computer network. Image retrieval system was developed purposely to provide an efficient tool for a set of images from a collection of images in the database that matches the user’s requirements in similarity evaluations such as image content similarity, edge, and color similarity. Retrieving images based on the content which is color, texture, and shape is called content based image retrieval (CBIR). The content is actually the feature of an image and these features are extracted and used as the basis for a similarity check between images. The algorithms used to calculate the similarity between extracted features. There are two kinds of content based image retrieval which are general image retrieval and application specific image retrieval. For the general image retrieval, the goal of the query is to obtain images with the same object as the query. Such CBIR imitates web search engines for images rather than for text. For application specific, the purpose tries to match a query image to a collection of images of a specific type such as fingerprints image and x-ray. In this paper, the general architecture, various functional components, and techniques of CBIR system are discussed. CBIR techniques discussed in this paper are categorized as CBIR using color, CBIR using texture, and CBIR using shape features. This paper also describe about the comparison study about color features, texture features, shape features, and combined features (hybrid techniques) in terms of several parameters. The parameters are precision, recall and response time. </div>
文摘In medical research and clinical diagnosis, automated or computer-assisted classification and retrieval methods are highly desirable to offset the high cost of manual classification and manipulation by medical experts. To facilitate the decision-making in the health-care and the related areas, in this paper, a two-step content-based medical image retrieval algorithm is proposed. Firstly, in the preprocessing step, the image segmentation is performed to distinguish image objects, and on the basis of the ...
基金Supported by the National Natural Science Foundation of China under Grant No. 60473077 and the Program for New Century Excellent Talents in University.
文摘In recent years there has been a significant interest in peer-to-peer （P2P） environments in the community of data management. However, almost all work, so far, is focused on exact query processing in current P2P data systems. The autonomy of peers also is not considered enough. In addition, the system cost is very high because the information publishing method of shared data is based on each document instead of document set. In this paper, abstract indices （AbIx） are presented to implement content-based approximate queries in centralized, distributed and structured P2P data systems. It can be used to search as few peers as possible but get as many returns satisfying users＇ queries as possible on the guarantee of high autonomy of peers. Also, abstract indices have low system cost, can improve the query processing speed, and support very frequent updates and the set information publishing method. In order to verify the effectiveness of abstract indices, a simulator of 10,000 peers, over 3 million documents is made, and several metrics are proposed. The experimental results show that abstract indices work well in various P2P data systems.
