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P1 | |
why ? | |
Too many images, how to search ? | |
a. human annotation impractical | |
b. Content Based Image Retrieval - cbir identifies low-level features (color/shape/texture), | |
need higher level because SEMANTIC GAP | |
SEMANTIC GAP= (gap b/w lower-level features & semantic concepts used by humans to describe images) | |
c. Automatic Image Annotation | |
main idea of AIA : | |
automatically learn "semantic concept models" from large number of image samples | |
use the concept models to annotate new images. | |
=> images can be retrieved by keywords. | |
Img => Semantic Concept Model => Annotate. | |
P2 FE(Feature Extraction Review) | |
input image == unstructured array of pixels | |
1. FE from these pixels | |
2. region based FE >> global FE | |
3. region based FE needs Image Segmentation | |
Image Segmentation = want homogenous regions | |
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