Graph similarity algorithm evaluation

Nkgau, Tallman; Anderson, George

doi:10.1109/sai.2017.8252114

Cited by 3 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Semantic analysis based on graphs provides comprehensive query languages like SPARQL [48], RDF Schemas and many algorithms for graph transformations. In [49], a comparison of algorithms for graph similarity is given. AI-based methods are becoming more common [47].…”

Section: Graph Representation Processingmentioning

confidence: 99%

AI-Based Semantic Multimedia Indexing and Retrieval for Social Media on Smartphones

Wagenpfeil

Engel

Kevitt³

et al. 2021

Information

View full text Add to dashboard Cite

To cope with the growing number of multimedia assets on smartphones and social media, an integrated approach for semantic indexing and retrieval is required. Here, we introduce a generic framework to fuse existing image and video analysis tools and algorithms into a unified semantic annotation, indexing and retrieval model resulting in a multimedia feature vector graph representing various levels of media content, media structures and media features. Utilizing artificial intelligence (AI) and machine learning (ML), these feature representations can provide accurate semantic indexing and retrieval. Here, we provide an overview of the generic multimedia analysis framework (GMAF) and the definition of a multimedia feature vector graph framework (MMFVGF). We also introduce AI4MMRA to detect differences, enhance semantics and refine weights in the feature vector graph. To address particular requirements on smartphones, we introduce an algorithm for fast indexing and retrieval of graph structures. Experiments to prove efficiency, effectiveness and quality of the algorithm are included. All in all, we describe a solution for highly flexible semantic indexing and retrieval that offers unique potential for applications such as social media or local applications on smartphones.

show abstract

Section: Graph Representation Processingmentioning

confidence: 99%