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首页> 外文期刊>Indian Journal of Science and Technology >Investigation of Distance, Machine and Kernel Learning Similarity Methods for Visual Search in Content Based Image Retrieval
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Investigation of Distance, Machine and Kernel Learning Similarity Methods for Visual Search in Content Based Image Retrieval

机译：基于内容的图像检索中视觉搜索的距离，机器和内核学习相似性方法的研究

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Background/Objectives: The major objective of this work is to increase the retrieval accuracy of medical images by measuring the visual similarity of Content-Based Image Retrieval (CBIR) system. Methods/Statistical Analysis: This paper presents an On-line Multiple Kernel Similarity (OMKS) learning framework for performing classification based on kernel-based proximity functions. In accordance with many existing methods some other related issues are also discussed and retrieval performance evaluation of the existing and proposed OMKS learning strategy is also discussed. Findings: Several number of the distance based learning algorithm has been proposed in recent works. It is mainly aimed for the sake of measuring the visual similarity corresponding to the images. This paper involves in providing a comprehensive review of the technical achievements of distance learning, machine learning along with kernel learning methods for conducting visual similarity search. The methods cited have limitation in their capability of the similar measurement with complicated patterns in most practical applications. Similarity of multimodal data identified through the multiple resources 28 , cannot be handled. Application/Improvements: Evaluation of the technique proposed for CBIR is performed on a huge amount of image data sets where motivating results indicate that OMKS performs better than the state-of the- art techniques significantly. At last, based on OMKS technology and the rise of requisitions from practical-world applications, and idealistic future research directions have been identified as suggestions 29 .
机译：背景/目的：这项工作的主要目的是通过测量基于内容的图像检索（CBIR）系统的视觉相似性来提高医学图像的检索准确性。方法/统计分析：本文提出了一种在线多核相似度（OMKS）学习框架，用于基于基于内核的邻近函数进行分类。根据许多现有方法，还讨论了其他一些相关问题，还讨论了对现有和提出的OMKS学习策略的检索性能评估。发现：最近的工作中已经提出了几种基于距离的学习算法。其主要目的是为了测量与图像相对应的视觉相似度。本文涉及对远程学习，机器学习以及用于进行视觉相似性搜索的内核学习方法的技术成就的全面综述。在大多数实际应用中，所引用的方法在以复杂模式进行相似测量的能力方面存在局限性。无法处理通过多个资源28标识的多峰数据的相似性。应用/改进：针对大量图像数据集对CBIR提出的技术进行了评估，其动机结果表明OMKS的性能明显优于最新技术。最后，基于OMKS技术以及来自实际应用的需求的增长，理想的未来研究方向已被确定为建议29。

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