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A METHOD FOR RETRIEVING IMAGES BASED ON CONTENT BASED IMAGE RETRIEVAL

机译:基于基于内容的图像检索的图像检索方法

摘要

In one of the important aspect of the invention it is provided that a method for retrieval of images from databases, a method of retrieval of Content Based Image Retrieval (CBIR) deployed on Hybrid Cloud Architecture. CBIR is nothing but retrieving images from a database or library of digital images according to the visual content of the images such as of colors, textures or shapes; In a method for retrieving images related to a query image from a large set of distinct images. It follows an approach to extract the different shape and texture information present in an image. The above features which can be stored in vectors called feature vectors and therefore these are compared to the feature vectors of query image and the image information is sorted in decreasing order of similarity. The processing and storage of the same is done on cloud. The CBIR system is an application built on Windows Azure platform. It is a parallel processing problem where a large set of images have to be operated upon to rank them based on a similarity to a provided query image by the user. Image feature vector extraction is computationally heavy operation and it is performed on windows azure cloud by a Software as a service, the feature vectors are stored in blob storage on the private cloud, to further reduce the cost and security the image database is stored on private cloud built using Microsoft Azure private cloud setup, hence the system is implemented on Hybrid Cloud Architecture.
机译:在本发明的重要方面之一中,提供了一种用于从数据库检索图像的方法,一种在混合云架构上部署的基于内容的图像检索(CBIR)的检索方法。 CBIR只是根据图像的视觉内容(例如颜色,纹理或形状)从数字图像的数据库或数据库中检索图像;在用于从一大组不同图像中检索与查询图像有关的图像的方法。它遵循一种提取图像中存在的不同形状和纹理信息的方法。可以存储在称为特征向量的向量中的上述特征,因此将这些特征与查询图像的特征向量进行比较,并以相似度从高到低的顺序对图像信息进行排序。相同的处理和存储在云上完成。 CBIR系统是基于Windows Azure平台构建的应用程序。这是并行处理问题,其中必须基于用户与所提供的查询图像的相似性,对大量图像进行操作以对其进行排名。图像特征向量提取是一项计算量大的操作,它通过软件即服务在Windows Azure云上执行,特征向量存储在私有云上的Blob存储中,以进一步降低图像数据库存储在私有云上的成本和安全性使用Microsoft Azure私有云设置构建的云,因此该系统在混合云体系结构上实现。

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