Guided Hash Algorithm for Information Semantic Retrieval in Multimedia Environment

Abstract

With technological progress, how to efficiently process and retrieve large amounts of multi-modal multimedia data has become a challenge. Although multi-modal hash algorithms have been applied in this field, their performance potential has not been fully realized. To fully leverage the performance of the model, a supervised discrete multi-modal hash algorithm has been proposed to improve the efficiency of multi-modal retrieval. The adaptive online multi-modal hash algorithm is used to dynamically adapt to changes in query samples. The topological semantic multi-modal hash algorithm is applied to further improve the retrieval performance of multi-modal hashes. According to the results, in the MIR Flickr dataset, the Mean Average Precision (MAP) reaches 0.8048, 0.8155, and 0.8185 at 32-bit, 64-bit, and 128-bit, respectively. Fusion Graph Convolutional multi-modal Hashing (FGCMH) exhibits the bestin different datasets. From this, the designed method has high processing power in handling large-scale and high-dimensional multimedia data.

Keywords

• Multimedia
• semantics
• information retrieval
• hash algorithm
• self-adaption