Abstract
In this paper we propose a novel method for multimedia semantic indexing
using model vectors. Model vectors provide a semantic signature for
multimedia documents by capturing the detection of concepts broadly
across a lexicon using a set of independent binary classifiers. While
recent techniques have been developed for detecting simple generic
concepts such as indoors, outdoors, nature, manmade, faces, people,
speech, music, and so forth W.H. Adams et al., November 2002, these
labels directly support only a small number of queries. Model vectors
address the problem of answering queries for which relationships
to specific concepts is either unknown or indirect by developing
a basis across across the lexicon. In the simplest case, each model
vector dimension corresponds to the confidence score by which a corresponding
concept from the lexicon is detected. However, we show how other
information such as relevance, reliability and concept correlation
can also be incorporated. Overall, the model vectors can be used
in a variety of methods for multimedia indexing, including model-based
retrieval, relevance feedback searching and concept querying. In
this paper, we present the model vector method and study different
strategies for computing and comparing model vectors. We empirically
evaluate the retrieval effectiveness of the model vector approach
compared to other search methods in a large video retrieval testbed.
Links and resources
Tags
- MPEG-7,
- binary
- classifiers,
- communication,
- concept
- content-based
- database
- databases,
- detection,
- feedback
- feedback,
- indexing,
- lexicon
- model
- multimedia
- processing,
- querying,
- relevance
- reliability,
- retrieval,
- searching,
- semantic
- signal
- signature
- vectors,
- video
community