Classification of Feature Set Using K-means Clustering from Histogram Refinement Method

Abstract

In this paper, we propose to use K-means clustering for the classification of feature set obtained from the histogram refinement method. Histogram refinement provides a set of features for proposed for Content Based Image Retrieval (CBIR). 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. Hence histogram refinement method further refines the histogram by splitting the pixels in a given bucket into several classes based on color coherence vectors. Several features are calculated for each of the cluster and these features are further classified using the K-means clustering.

Wavelet Based Content Based Image Retrieval Using Color and Texture Feature Extraction by Gray Level Coocurence Matrix and Color Coocurence Matrix

“…For such queries, color histograms can be employed because they are very efficient regarding computations as well as they offer insensitivity to small changes regarding camera position (Youngeun et al, 2008). But the main problem with color histograms is their coarse characterization of an image.…”

“…Let us denote the largest cluster in each bin as Lαj, the median cluster in each bin as Mαj, the smallest cluster in each bin as Sαj and variance of clusters in each bin as Vαj. These features are used for the image retrieval (Youngeun et al, 2008).…”

“…In order to diminish the sensitivity of initial point choice, (Youngeun et al, 2008) we employ a mediod, which is the most centrally located object in a cluster, to obtain better initial centers. The sample to nearly represent the original dataset, that is to say, samples drawn from dataset can't cause distortion and can reflect original data's distribution.…”

“…The key technologies image retrieval include: Image feature extraction, feature-based similarity calculation, semantically relevance feedback and image acquisition (Youngeun et al, 2008). It relates to machine vision, pattern recognition, database technology and information retrieval studies.…”

An Efficient Indexing for Content Based Image Retrieval Based on Number of Clusters Using Clustering Technique

“…Color Color is one of the most common feature of an image used in Content-Based Image Retrieval [2,8,11].Color comparison between two images would however be time consuming and difficult problem for large database. To overcome this problem, some methods reduce the amount of information through which most of the information of an image is lost.…”