ENHANCED CBIR MECHANISM USING STEERABLE PYRAMID AND MEDIAN VECTOR ALGORITHM - Pooja, Pooja; Jindal, Sonika (INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2017)
Recently, digital content has become a significant and inevitable asset of or any enterprise and the need for visual content management is on the rise as well. Content-based image retrieval has attracted voluminous research in the last decade paving way for development of numerous techniques and systems besides creating interest on fields that support these systems. CBIR indexes the images based on the features obtained from visual content so as to facilitate speedy retrieval. In this thesis work, we present a steerable pyramid based image retrieval system that uses color, contours and texture as visual features to describe the content of an image region. We have initially used steerable pyramid to extract texture features from query image and database images and store them in feature vectors. Second, to speed up retrieval and similarity computation, the database images are classified and the extracted regions are clustered according to their feature vectors using median vector algorithm. This process is performed before query matching takes place. Therefore to answer a query our system does not need to search the entire database images; instead just a number of candidate images are required to be searched for image similarity.Â Our proposed system has the advantage of increasing the retrieval accuracy and decreasing the retrieval time. The experimental evaluation of the system is based on a satellite and medical image database. From the experimental results, it is evident that our system performs significantly better and faster compared with other existing systems. In our analysis, we provide a comparison between retrieval results based on features extracted from the whole image using steerable pyramid with median vector and features extracted from same image without median vector. The results demonstrate that each type of feature is effective for a particular type of images according to its semantic contents, and using a combination of them giving better retrieval results for almost all different classes of images in the dataset.Â Â