IMAGE CLASSIFICATION BY K-MEANS CLUSTERING
SYNOPSIS
INTRODUCTION
In a content based image retrieval system, target images are sorted by feature similarities
with respect to the query (CBIR).In this paper, 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.
BACK GROUND
Color histograms are widely used for retrieval of results based on queries. 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. But
the main problem with color histograms is their coarse characterization of an image. That may
itself result in same histograms for images with different appearances. Color histograms are
employed in systems such as QBIC, Chabot etc. They all utilize the advantages of color
histogram. In this paper, a modified scheme based on color histogram is used. This modified
method is based on histogram refinement . The histogram refinement method provides that the
pixels within a given bucket be split into classes based upon some local property and these split
histograms are then compared on bucket by bucket basis just like normal histogram matching but
the pixels within a bucket with same local property are compared. So the results are better than
the normal histogram matching. So not only the color features of the image are used but also the
spatial information is incorporated to refine the histogram.
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In existing system the standard histograms are used. 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.
PROPOSED METHODOLOGY
In this project, 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). Histogram refinement method further
refines the histogram by splitting the pixels in a given bucket into several classes and producing
the comparison graph of 8-bin (bucket) and 16 bin.
Block Diagram
Fig: Block diagram for image classification using k means clustering
Head office: 2nd floor, Solitaire plaza, beside Image Hospital, Ameerpet, Hyderabad
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References
1. Youngeun An, Junguk Baek etal, “Classification of Feature set using K-means Clustering
from Histogram Refinement method”, IEEE 2008.
2. Donn Morrison, Stephane Marchand –Maillet, Eric Bruno, “ Semantic clustering of
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description for content based Image retrieval” ,IEEE 2008.
4. Raquel E.Patino-Escarcina and Jose Alfredoferreira costa, “The semantic clustering of
images and its relation with low level color features”, IEEE 2008.
5. Yixin Chen, James Z. Wang, Krovetz, “CLUE: cluster-based retrieval of images by
unsupervised learning”, IEEE Transaction on Image Processing vol.14, No.8, August
2005.
6. Xiaoxin Yin, Mingjing Li, Lei Zhang, Hongjiang Zhang, ” Semantic image clustering
using relevance feedback”, IEEE 2003.
7. Gholamhosein, Sheikholeslami et al., “Semquery: Semantic Clustering and Querying on
Heterogeneous Features For Visual Data”, IEEE Transaction on Knowledge and Data
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8. J.Z.Wang, J. Li, and G.Wiederhold. Simplicity: semantics sensitive integrated matching
for picture libraries. IEEE Trans. Pattern Anal. Mach. Intell., 23(9):947–963, 2001
Head office: 2nd floor, Solitaire plaza, beside Image Hospital, Ameerpet, Hyderabad
www.kresttechnology.com, E-Mail : krestinfo@gmail.com , Ph: 9885112363 / 040 44433434
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