An effective technique for the content based image retrieval to reduce the semantic gap based on an optimal classifier technique
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- 作者：Pranoti P. Mane ; Narendra G. Bawane
- 关键词：content based image retrieval (CBIR) ; relevance feedback (RF) ; k ; means algorithm ; artificial bee colony (ABC) ; artificial neural network (ANN)
- 刊名：Pattern Recognition and Image Analysis
- 出版年：2016
- 出版时间：July 2016
- 年：2016
- 卷：26
- 期：3
- 页码：597-607
- 全文大小：2,044 KB
- 刊物类别：Computer Science
- 刊物主题：Pattern Recognition
Image Processing and Computer Vision
Russian Library of Science - 出版者：MAIK Nauka/Interperiodica distributed exclusively by Springer Science+Business Media LLC.
- ISSN：1555-6212
- 卷排序：26
文摘
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 “semantic gap” 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.