Towards Semantic Clustering : Grouping Image Visual Features Through Exploratory Factor Analysis
Current image clustering schemes tend to cluster images based on similarity of low-level image visual features. Our previous work has demonstrated the need for organizing groups of low-level image visual features into composite feature sets that can then be mapped to semantically relevant abstr...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/30109/1/Towards%20Semantic%20Clustering%20-%20Copy.pdf http://ir.unimas.my/id/eprint/30109/ |
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| Institution: | Universiti Malaysia Sarawak |
| Language: | English |
| Summary: | Current image clustering schemes tend to cluster images
based on similarity of low-level image visual features.
Our previous work has demonstrated the need for
organizing groups of low-level image visual features
into composite feature sets that can then be mapped to
semantically relevant abstractions. Symbolic terms such
as wing ratio and tailed-wings and many more have been
obtained from mapping clusters from a single-feature
clustering and visual knowledge acquisition. Current
focus is the explorations on the extraction and
transformation of groupings of low-level image visual
features into factor space before mapped to these
meaningful terms. Preliminary results from exploratory
factor analyses with different settings suggested the
solution of forming four groups of features. The selected
visual feature groupings have also been shown to
correspond to the user-relevant symbolic terms. We
hope to highlight these mapped relationships at the
conference. |
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