Title: Multimedia auto-annotation via label correlation mining

Authors: Feng Tian; Fuhua Shang; Ning Sun

Addresses: School of Computer and Information Technology, Northeast Petroleum University, DaQing 163318, China ' School of Computer and Information Technology, Northeast Petroleum University, DaQing 163318, China ' School of Computer and Information Technology, Northeast Petroleum University, DaQing 163318, China

Abstract: How to automatically determine the label for multimedia object is crucial for multimedia retrieval. The neighbour voting mechanism is known to be effective for multimedia object annotation. However, it estimates the relevance of a label with respect to multimedia content by labels' frequency derived from its nearest neighbours, which does not take into account the assigned label set as a whole. We propose LSLabel, a novel algorithm that achieves comparable results with label correlation mining. By incorporating the label correlation and label relevance with respect to multimedia content, the problem of assigning labels to multimedia object is formulated into a joint framework. The problem can be efficiently optimized in a heuristic manner, which allows us to incorporate a large number of feature descriptors efficiently. On two standard real world datasets, we demonstrate that LSLabel matches the current state-of-the-art.

Keywords: label correlation; multimedia annotation; auto-annotation; correlation mining.

DOI: 10.1504/IJHPCN.2019.099266

International Journal of High Performance Computing and Networking, 2019 Vol.13 No.4, pp.427 - 435

Received: 14 Jul 2016
Accepted: 01 Nov 2016
Published online: 24 Apr 2019 *

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