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Content-based medical image retrieval is now getting more and more attention in the world, a feasible and efficient retrieving algorithm for clinical endoscopic images is urgently required. Methods: Based on the study of single feature image retrieving techniques, including color clustering, color texture and shape, a new retrieving method with multi-features fusion and relevance feedback is proposed to retrieve the desired endoscopic images. Results: A prototype system is set up to evaluate the proposed method’s performance and some evaluating parameters such as the retrieval precision & recall, statistical average position of top 5 most similar image on various features, etc. are therefore given. Conclusions: The algorithm with multi-features fusion and relevance feedback gets more accurate and quicker retrieving capability than the one with single feature image retrieving technique due to its flexible feature combination and interactive relevance feedback.
Date : 2025-12-28 Size : 351kb User : gokul/goks

查准率与查全率的计算代码。使用matlab编写完成的。可以计算图像标注的查准率与查全率。-Precision and recall rate calculation code. Prepared using matlab complete. Image annotation can be calculated with precision recall rate.
Date : 2025-12-28 Size : 1kb User : 韩昌刚

图像处理的查准率和查全率的代码,适合用于图像图像的检测,目标的检测的评估-Image processing precision and recall rate code
Date : 2025-12-28 Size : 1kb User : 冯艳红

图像分割,显著性算法的precision,recall 和ROC的评价指标计算。-Image Segmentation, significant precision,recall and ROC assessment index calculation algorithm.
Date : 2025-12-28 Size : 2kb User : 李恒
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