Rapid cross-modal retrieval method and system for incremental data carrying new categories

Abstract

The invention provides a rapid cross-modal retrieval method and system for incremental data carrying new categories, and the method comprises an incremental hash learning step: extracting a binary representation of a known category label from a known hash code stored in a multimedia known category database, and then according to a similarity relationship between the known category label and an unknown category label, obtaining a binary representation of an unknown category label to supervise the generation of a hash code of incremental data in an incremental category database; and a Hash function learning step: in a Hash function learning process, obtaining an anchor point set from the known category database and the incremental category database through sampling, updating parameters of the deep network by adopting an asymmetric strategy based on the anchor point set, and carrying out Hash function learning to obtain a required model. The method can directly learn the hash code of the unknown incremental data under the condition of keeping the hash code of the original data unchanged, so the mode requirement of rapid training can be met.

Description

technical field [0001] The invention belongs to the technical fields of cross-modal retrieval and deep learning, and in particular relates to a fast cross-modal retrieval method and system for incremental data carrying new categories. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] With the development of the Internet, multimedia data such as images, texts, videos, and audios generated by various smart terminal devices and websites show an explosive growth trend. Faced with these rich and massive multimedia data, how to quickly and accurately retrieve multi-modal data in the huge database according to user needs is a hot spot in the research of multimedia information retrieval. Therefore, cross-modal retrieval emerges as the times require, and its main purpose is to use query data of one modality to retrieve similar data of another modali...

AI Technical Summary

Problems solved by technology

[0005] However, the existing deep cross-modal hashing methods cannot solve the problem of incremental learning well

In other words, most deep cross-modal hashing methods cannot adapt to the incremental data of emerging unknown category labels in time, and need to be fed to the network at the same time as the original data to retrain the model

This mode has the following disadvantages: First, retraining all data means completely ignoring the hash code of the original data obtained through previous training, wasting computing resources and training time; in addition, as more and more unknown categories The generation of incremental data of tags, feeding the original data and incremental data to the network at the same time will increase the computational complexity of the model, which violates the demand for fast and accurate retrieval of large-scale multimedia data; and the current incremental hash learning Method cannot handle cross-modal retrieval issues

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