Research Of Image Retrieval Algorithm Based On Color And SIFT Featurs And Parallelization

Image retrieval has become one of the most important means of obtaining information.How to get content from massive images quickly and accurately turns into main bottleneck in the development of image retrieval.Therefore,the paper focuses on how to design retrieval algorithm,construct image retrieval system and improve the performance.The work mainly comprises there parts,i.e.analysis of image feature,design of retrieval algorithm and parallel implementation based on Hadoop platform.Firstly,the paper reviews the development and achievement of image retrieval technology,discussesimage retrieval technology based on text,content and high-level semantics and analyses their advantages,disadvantages and application scenarios.Then,a method combining color auto-correlogram feature and SIFT feature is proposed for image retrieval.Secondly,DBSCAN is introduced to cluster the images based on color auto-correlogram.Then the SIFT features are extracted and matched in the cluster to reduce the time complexity.Next,instead of the standard similarity measure between key points,average Euclidean distance is adopted to improve the accuracy.The paper implements the image retrieval system based on MapReduce to ensure the efficiency.The features extraction and similarity computation is decomposed into a series of map and reduce.The DBSCAN algorithms is also designed and implemented on the Hadoop platform.Finally,the experiment is designed to validate the performance of the system.The results show that the algorithms proposed in the paper are feasible and speedup on the Hadoop platform.