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
With the advance of multimedia technology and communications, images and videos become the major streaming information through the Internet. How to fast retrieve desired similar images precisely from the Internet scal...With the advance of multimedia technology and communications, images and videos become the major streaming information through the Internet. How to fast retrieve desired similar images precisely from the Internet scale image/video databases is the most important retrieval control target. In this paper, a cloud based content-based image retrieval （CBIR） scheme is presented. Database-categorizing based on weighted-inverted index （DCWII） and database f＇dtering algorithm （DFA） is used to speed up the features matching process. In the DCWII, the weights are assigned to discrete cosine transform （DCT） coefficients histograms and the database is categorized by weighted features. In addition, the DFA filters out the irrelevant image in the database to reduce unnecessary computation loading for features matching. Experiments show that the proposed CBIR scheme outperforms previous work in the precision-recall performance and maintains mean average precision （mAP） about 0.678 in the large-scale database comprising one million images. Our scheme also can reduce about 50% to 85% retrieval time by pre-filtering the database, which helps to improve the efficiency of retrieval systems.展开更多
In this research, a content-based image retrieval （CBIR） system for high resolution satellite images has been developed by using texture features. The proposed approach uses the local binary pattern （LBP） texture ...In this research, a content-based image retrieval （CBIR） system for high resolution satellite images has been developed by using texture features. The proposed approach uses the local binary pattern （LBP） texture feature and a block based scheme. The query and database images are divided into equally sized blocks, from which LBP histograms are extracted. The block histograms are then compared by using the Chi-square distance. Experimental results show that the LBP representation provides a powerful tool for high resolution satellite images （HRSI） retrieval.展开更多
提出一种单类支持向量机(One-Class Support Vector Machine,OC-SVM)和主动学习相结合的图像内容检索方法。在相关反馈过程中选择最具信息的样本训练支持向量机,在保证检索机性能的前提下,可有效减少相关反馈的次数和所需学习样本的数量...提出一种单类支持向量机(One-Class Support Vector Machine,OC-SVM)和主动学习相结合的图像内容检索方法。在相关反馈过程中选择最具信息的样本训练支持向量机,在保证检索机性能的前提下,可有效减少相关反馈的次数和所需学习样本的数量,通过两者的互补来有效地提高图像检索的精度。试验表明,该方法较好地解决了图像检索中的小样本、样本不对称及反馈样本选择问题,能够显著提高图像检索的效率和性能。展开更多
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"MOST"under Grant No.104-2221-E-011-056
文摘With the advance of multimedia technology and communications, images and videos become the major streaming information through the Internet. How to fast retrieve desired similar images precisely from the Internet scale image/video databases is the most important retrieval control target. In this paper, a cloud based content-based image retrieval （CBIR） scheme is presented. Database-categorizing based on weighted-inverted index （DCWII） and database f＇dtering algorithm （DFA） is used to speed up the features matching process. In the DCWII, the weights are assigned to discrete cosine transform （DCT） coefficients histograms and the database is categorized by weighted features. In addition, the DFA filters out the irrelevant image in the database to reduce unnecessary computation loading for features matching. Experiments show that the proposed CBIR scheme outperforms previous work in the precision-recall performance and maintains mean average precision （mAP） about 0.678 in the large-scale database comprising one million images. Our scheme also can reduce about 50% to 85% retrieval time by pre-filtering the database, which helps to improve the efficiency of retrieval systems.
文摘In this research, a content-based image retrieval （CBIR） system for high resolution satellite images has been developed by using texture features. The proposed approach uses the local binary pattern （LBP） texture feature and a block based scheme. The query and database images are divided into equally sized blocks, from which LBP histograms are extracted. The block histograms are then compared by using the Chi-square distance. Experimental results show that the LBP representation provides a powerful tool for high resolution satellite images （HRSI） retrieval.
文摘提出一种单类支持向量机(One-Class Support Vector Machine,OC-SVM)和主动学习相结合的图像内容检索方法。在相关反馈过程中选择最具信息的样本训练支持向量机,在保证检索机性能的前提下,可有效减少相关反馈的次数和所需学习样本的数量,通过两者的互补来有效地提高图像检索的精度。试验表明,该方法较好地解决了图像检索中的小样本、样本不对称及反馈样本选择问题,能够显著提高图像检索的效率和性能。
