Image retrieval method integrating classification with hash partitioning and image retrieval system utilizing same

Abstract

The invention discloses an image retrieval system integrating classification with hash partitioning, which comprises a downloading module, a classification module training module, an image classification module, a characteristic extraction module, a recording table building module, a partitioning module, a request processing module, a retrieval module, a similarity acquiring module and a result returning module. The downloading module is used for downloading images so as to build an image library, the classification model training module classifies the images in the image library according tothe shapes and selects representative sample images from the image library to form a sample library firstly, then extracts characteristic descriptors of the classification bottom layer of all images in the sample library and performs trainings on the characteristic descriptors of the classification bottom layer by a support vector machine so as to obtain discriminant of each classification. Classification models are formed according to the discriminants of all the classifications. Precision ratio of the image retrieval system is increased, the problem of low recall ratio during classificationmistakes is overcome, and the retrieval speed of the image retrieval system is increased integrally.

Description

technical field [0001] The invention relates to the field of content-based image vertical retrieval, more specifically, the invention relates to an image retrieval method and an image retrieval system that integrate classification and global index. Background technique [0002] The existing content-based image retrieval mainly includes retrieval based on classification, retrieval based on clustering and retrieval based on global index. Classification-based retrieval is to classify the pictures in the database in advance. When searching, first obtain the category of the query picture, and then retrieve similar pictures in the category; cluster-based retrieval is to cluster all picture features to form a cluster center , the image to be queried first looks for the nearest cluster center during retrieval, and then searches for similar images in the image collection corresponding to the cluster center; the retrieval based on the global index is to index all image features, and t...

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Problems solved by technology

[0003] However, the existing content-based image retrieval methods have the following problems: when the clustering method is used, the accuracy of the query is low due to the loss of the accuracy of the feature vector due to clustering and indexing; when the classification method is used, if the image to be queried is classified incorrectly , the precision rate and recall rate will be greatly reduced; when using the index method, the index established on the feature vector of the image has a relatively slow query speed, which will lead to time-consuming retrieval of the system

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