Cross-media sparse Hash indexing method

Abstract

The invention discloses a cross-media sparse Hash indexing method. The method comprises the steps of (1) performing unified modeling on incidence relations among data of a plurality of modes through hyper-graphs; (2) learning dictionaries of the plurality of modes simultaneously through a dictionary learning frame, applying regular constraint to sparse and hyper-graph incidence relations and learning data of each mode to obtain corresponding dictionaries; (3) using the learned dictionaries as Hash functions, and performing Hash encoding on new data through corresponding mode dictionaries; (4) converting the Hash codes into sparse code sets through corresponding Hash strategies to change sparse code similarity calculation problems into the set similarity calculation problems, and performing similarity calculation through a similar jaccard distance measurement mode.

Description

technical field

[0001] The invention relates to cross-media retrieval, in particular to a cross-media efficient indexing method based on massive data. Background technique

[0002] With the rapid development of Internet technology and the popularity of social networks, the amount of multimedia data on the network is growing at an alarming rate. Multimedia data has complex semantics and is difficult to measure directly. The general method is to perform feature extraction on it to obtain corresponding features. Usually these features are high-dimensional, so the multimedia data retrieval problem is transformed into a high-dimensional data retrieval problem. The time complexity of the traditional brute force linear comparison strategy obviously cannot meet the online search needs of users in the face of large-scale high-dimensional data. At this time, people need an effective indexing mechanism to achieve efficient retrieval of high-dimensional data. Hash method is a hot res...

Images