Sreeraj, M; Muhammed Anees, V; Santhosh Kumar, G(IEEE, December 7, 2012)
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Abstract:
In recent years there is an apparent shift in research
from content based image retrieval (CBIR) to automatic
image annotation in order to bridge the gap between low level
features and high level semantics of images. Automatic Image
Annotation (AIA) techniques facilitate extraction of high level
semantic concepts from images by machine learning techniques.
Many AIA techniques use feature analysis as the first step to
identify the objects in the image. However, the high dimensional
image features make the performance of the system worse. This
paper describes and evaluates an automatic image annotation
framework which uses SURF descriptors to select right number
of features and right features for annotation. The proposed
framework uses a hybrid approach in which k-means clustering
is used in the training phase and fuzzy K-NN classification in
the annotation phase. The performance of the system is evaluated
using standard metrics.