An Efficient Video Indexing and Retrieval Algorithm using Ensemble Classifier

Gayathri, N.; Mahesh, K.

doi:10.1109/iceeccot46775.2019.9114831

Cited by 4 publications

(1 citation statement)

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“…In the multimedia composition phase, numerous algorithms for the lower-level of feature extraction, such as SIFT or Haralick, have been defined, implemented and applied to multimedia content [21,32,55,56]. Segmentation algorithms such as MSER or SIMSER [32,[57][58][59] help to identify the correct bounding boxes of objects within images, and several ontology-based models for semantic annotation have been proposed in [32,[60][61][62][63]. Recent approaches regarding feature fusion in special topic areas are contributing to the approach; we are taking [64,65].…”

Section: Related Workmentioning

confidence: 99%

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

Wagenpfeil

Engel

Kevitt³

et al. 2021

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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.

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