An Effective Technique for the Content based Image Retrieval to Reduce the Semantic Gap based on an Optimal Classifier Technique
Pranoti P. Mane, Narendra G. Bawane
Journal of Multimedia Processing and Technologies, Vol. 7, No. 1, 2016.
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Abstract:
Content Based Image Retrieval (CBIR) systems use Relevance Feedback (RF) in order to improve the retrieval
accuracy. Research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing
the â€˜semantic gapâ€™ between the visual features and the richness of human semantics. In this paper, a novel system is
proposed to enhance the gain of long-term relevance feedback. In the proposed system, the general CBIR involves two
steps- ABC based training and image retrieval. First, the images other than the query image are pre-processed using
median filter and gray scale transformation for removal of noise and resizing. Secondly, the features such as Color,
Texture and shape of the image are extracted using Gabor Filter, Gray Level Co-occurrence Matrix and Hu-Moment
shape feature techniques and also extract the static features like mean and standard deviation. The extracted features
are clustered using k-means algorithm and each cluster are trained using ANN based ABC technique. A method using
artificial bee colony (ABC) based artificial neural network (ANN) to update the weights assigned to features by
accumulating the knowledge obtained from the user over iterations. Eventually, the comparative analysis performed
using the commonly used methods namely precision and recall were clearly shown that the proposed system is suitable
for the better CBIR and it can reduce the semantic gap than the conventional systems.