A Real-time Image Retrieval Method Based on Text Index

Abstract

The invention provides a real-time image retrieval method based on a text index. The method includes the steps of establishing a dictionary tree, obtaining all feature vectors vq to form an image inquiry library, extracting image feature points of input images, generating feature descriptors, sequencing the feature descriptors of the input images to serve as feature vectors vd expressed by a feature dictionary, calculating the similarity sim (vq, vd) of the feature vectors in an image library and the sequenced feature vectors of the input images, verifying M retrieved images, verifying the retrieval result and the like. By means of the method, image features are changed into text vectors, the feature retrieval method is changed into the text retrieval method, the retrieval speed can reach the real-time speed, deeper verification is conducted on the recognition result, and the method has higher robustness.

Description

technical field [0001] The invention relates to an image retrieval method, in particular to a text index-based real-time image retrieval method. Background technique [0002] With the rapid development of computer vision and search engines, image retrieval technology has received more and more attention in various industries. Especially now that the emergence of deep learning has made the research on image retrieval become a hot spot again. [0003] Traditional image retrieval methods mainly include template matching and feature matching. However, as the amount of data increases, this type of algorithm will increase the amount of calculation and memory, making it impossible to retrieve large data images. With the increase of the library, its retrieval time will also increase accordingly. When the amount of data reaches a certain level, this type of algorithm cannot be realized in real time, which greatly limits the scope of its use. Later, BOW proposed a method of converti...

AI Technical Summary

Problems solved by technology

However, the traditional method uses SIFT, SURF and other features to extract feature points, which makes the algorithm take a long time to build a visual lexicon.

The proposal of deep learning, which is very popular today, enables image retrieval to achieve a high recognition rate. However, due to the large amount of data required by deep learning, the training time is very long, and the memory requirements are large, it cannot be used in many occasions.

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