30-07-2012, 01:29 PM
IMAGE CLASSIFICATION BY K MEANS CLUSTERING
ABSTRACT
During the last decade, the advances in information technology allowed the development of content-based image retrieval (CBIR) systems, capable of retrieving images based on their similarity with one or more query images. Indicative examples of such systems are QBIC. . Specialized CBIR systems have been developed to support the retrieval of various kinds of images, including high- resolution computed tomographic (HRCT) images , breast cancer biopsy slides , positron emission tomographic (PET) functional images ultrasound images , pathology images, and radiographic images.
In a content based image retrieval system, target images are sorted by feature similarities with respect to the query (CBIR). we propose to use K-means clustering for the classification of feature set obtained from the histogram. Histogram provides a set of features for proposed for Content Based Image Retrieval (CBIR). Hence histogram method further refines the histogram by splitting the pixels in a given bucket into several classes.
Here we compute the similarity for 8 bins and similarity for 16 bins. Standard histograms, because of their efficiency and insensitivity to small changes, are widely used for content based image retrieval. But the main disadvantage of histograms is that many images of different appearances can have similar histograms because histograms provide coarse characterization of an image.
Result: Therefore the image classification in the content based image retrieval systems have successfully executed by K-means clustering.
Conclusion: we use K-means clustering for the classification of feature set obtained from the histogram. Histogram provides a set of features for proposed for Content Based Image Retrieval (CBIR). Histogram method refines the histogram by splitting the pixels in a given bucket into several classes.