Design And Implementation Of Large-scale Image Retrieval Based On Content

The rapid development of Internet and multimedia technologies in order to make an image as the representative of multimedia data showing explosive growth. Quick access to specific target images are really interested in a large set of images, especially those difficult to image using traditional text to articulate has become an increasingly important issue for many application environments to face. In this context, this paper designed and implemented a large-scale prototype image retrieval system based on content. The main research contents and system solutions of this article as follows:Comprehensive Comparative Study of Two mainstream image retrieval system:a complete feature-image retrieval system, characterized by extraction and compressed way to store images in the database of all the images, with each feature of the target image accurate searchable database; bag of words-Image Retrieval system, based on the quantitative characteristics into visual vocabulary, and a histogram of a visual vocabulary to express the image. Describes the current realization of several methods used by the two systems:Kd-Trees, Locality SensitiveHashing, Hierarchical K-Means, Inverted File, Min-Hash. Given the different nature and results of carrying out large-scale image retrieval methods. Experimental results show that the complete feature-image retrieval systems use more memory but has a high recognition rate; and the bag of words-although the performance of image retrieval system memory usage is relatively low but good.For a complete feature high long running time and storage requirements of the problem, we propose a corresponding large-scale image retrieval system solution: compression Kd-Trees image retrieval system. The method uses a compression binary label to compress the image local features, while using Kd-Trees for fast nearest neighbor search, thereby reducing the characteristics stored in the database, the compression characteristics of information. The system runs on four actual image data test scenarios The experimental results show that the compression Kd-Trees can reduce the expression of full-featured memory usage and run-time method, and can achieve higher retrieval performance.For the lower bag of words express way to identify performance problems, we propose the corresponding large-scale image retrieval system solution: multi-dictionary word pocket-image retrieval system. The method is based more visual words using different independent dictionaries, rather than adding the same idea more word dictionary. Runs in four actual image data in the system test scenarios, test results show that the multi-dictionary word bags at an increased storage and computing capacity of the premise, can significantly boost recognition performance bag of words reaches full-featured level of expression methods.