Paper Info
Title
Boosting cross-media retrieval via visual-auditory feature analysis and relevance feedback
Abstract
Different types of multimedia data express high-level semantics from different aspects. How to learn comprehensive high-level semantics from different types of data and enable efficient cross-media retrieval becomes an emerging hot issue. There are abundant statistical and semantic correlations among heterogeneous low-level media content, which makes it challenging to query cross-media data effectively. In this paper, we propose a new cross-media retrieval method based on short-term and long-term relevance feedback. Our method mainly focuses on two typical types of media data, i.e. image and audio. First, we build multimodal representation via statistical canonical correlation between image and audio feature matrices, and define cross-media distance metric for similarity measure; then we propose optimization strategy based on relevance feedback, which fuses short-term learning results and long-term accumulated knowledge into the objective function. Experiments on image-audio dataset have demonstrated the superiority of our method over several existing algorithms.
Year
DOI
Venue
2014
10.1145/2647868.2654975
ACM Multimedia 2001
Keywords
Field
DocType
cross-media retrieval,feature analysis,relevance feedback,retrieval models
Relevance feedback,Information retrieval,Similarity measure,Computer science,Canonical correlation,Metric (mathematics),Data type,Artificial intelligence,Boosting (machine learning),Semantics,Machine learning,Pattern recognition (psychology)
Conference
Citations 
PageRank 
References 
6
0.40
11
Authors
4
Name
Order
Citations
PageRank
Hong Zhang1144.54
Junsong Yuan23703187.68
Xingyu Gao3172.29
Zhenyu Chen460.40