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\title{Probabilistic Color Image Classifier based on Volumetric Robust Features}
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\begin{document}

             \author[1]{V.  Padmanabhan}

             \affil[1]{  Karpagam University}

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\date{\small \em Received: 9 December 2012 Accepted: 4 January 2013 Published: 15 January 2013}

\maketitle


\begin{abstract}
        


Need of more sophisticated methods to handle color images becomes higher due to the usage, size and volume of images. To retrieve and index the color images there must be a proper and efficient indexing and classification method to reduce the processing time, false indexing and increase the efficiency of classification and grouping.  We propose a new probabilistic model for the classification of color images using volumetric robust features which represents the color and intensity values of an region. The image has been split into number of images using box methods to generate integral image. The generated integral image is used to compute the interest point and the interest point represent the volumetric feature of an integral image. With the set of interest points computed for a source image, we compute the probability value of other set of interest points trained for each class to come up with the higher probability to identify the class of the input image. The proposed method has higher efficiency and evaluated with 2000 images as data set where 70 % has been used for training and 30% as test set.

\end{abstract}


\keywords{robust features, image classification, probabilistic classifier..}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
mage information systems are becoming increasingly important with the advancements in broadband networks, high-powered workstations etc. Large collections of images are becoming available to the public, from photo collection to web pages, or even video databases. Since visual media requires large amounts of memory and computing power for processing and storage, there is a need to efficiently index and retrieve visual information from image database. In recent years, image classification has become an interesting research field in application.\par
A number of image features based on color and texture attributes have been reported in literature. Although quantifying their discrimination ability to classification problem has not been so easy. Among the many possible features for classification purpose, extracted from an image. We focus on robust features like color distribution, density features, region features. The reason why we use three different features is the color distribution represent the distribution of color values throughout the image and region feature represent the features spread on a particular region where the density The image classification depends on variety of feature where the classification accuracy sit on the type of feature we used. The features of the image are extracted to compute some value which is called feature vector to represent the image in huge space. The classification is performed by computing any form of relevancy with set of feature vectors in the literature. There are many features has been used in the literature to compute the distance for classification.\par
The probabilistic classifier is one where there are more number of classes with large data set and basically the color images has more values and features. Classifying the color images are not an easy task, the probabilistic classifier computes the probability of input image which tells the relationship of image towards a class in probability manner. In most cases the probability based classifier has produced efficient results with less time complexity.\par
Efficient indexing and retrieval of large number of color images, classification plays an important and challenging role. The main focus of this research work is devoted to finding suitable representation for images and classification generally requires comparison of images depending on the certain useful features. 
\section[{II.}]{II.} 
\section[{Background}]{Background}\par
There are various methods have been discussed and we explore few of the methods for understanding and relate to our problem.\par
Efficient HIK SVM Learning for Image Classification [5], presents contributions concerning HIK SVM for image classification. First, we propose intersection coordinate descent (ICD), a deterministic and scalable HIK SVM solver. ICD is much faster than, and has similar accuracies to, general purpose SVM solvers and other fast HIK SVM training methods. We also extend ICD to the efficient training of a broader family of kernels. Second, we show an important empirical observation that ICD is not sensitive to the C parameter in SVM, and we provide some theoretical analyses to explain this observation. ICD achieves high accuracies in many problems, using its default parameters. This is an attractive property for practitioners, because many image processing tasks are too large to choose SVM parameters using crossvalidation. Improving Color Constancy Using Indoor-Outdoor Image Classification \hyperref[b4]{[6]}, designed different strategies for the selection and the tuning of the most appropriate algorithm (or combination of algorithms) for each class. We also considered the adoption of an uncertainty class which corresponds to the images where the indoor/outdoor classifier is not confident enough.\par
The illuminant estimation algorithms considered here are derived from the framework recently proposed by Van de Weijer and Gevers. We present a procedure to automatically tune the algorithms' parameters.\par
Iris image classification based on color information \hyperref[b5]{[7]}, we propose a novel color feature for iris classification, named as iris color Texton using RGB, Color Local Texture Features for Color Face Recognition \hyperref[b7]{[9]}, proposed color local texture features are able to exploit the discriminative information derived from spatio chromatic texture patterns of different spectral channels within a certain local face region.\par
Furthermore, in order to maximize a complementary effect taken by using color and texture information, the opponent color texture features that capture the texture patterns of spatial interactions between spectral channels are also incorporated into the generation of CLGW and CLBP. In addition, to perform the final classification, multiple color local texture features (each corresponding to the associated color band) are combined within a feature-level fusion framework.\par
The most of the related methods have classification errors and to overcome the demerits we propose a new probabilistic approach using volumetric estimations. 
\section[{III.}]{III.} 
\section[{Proposed Method}]{Proposed Method}\par
The proposed method has three phases namely sub image generation, interest point computation, and probabilistic image classifier. At the first stage an image is converted to set of small images, at the second stage the images intensity and color features are extracted to compute interest point and at the final stage the probability value is computed for each class for the set of interest points computed based on which the image is assigned with a class. 
\section[{IV.}]{IV.} 
\section[{Integral Image Generation}]{Integral Image Generation}\par
In order to improve matching accuracy and faster processing, we compute the integral images. The integral images are the small set of images generated using box filters which splits images into many number of sub image set. The input image is selected and number of sub images is created based on the parameters m and n. Here m and n specifies the width and height of the integral image to be generated. The value of m and n is a multiple of width and height of the image. For example for a image with size 300×300, the value of m and n will be 3×5 or 5×3 and so on. V. 
\section[{Interest Point Computation}]{Interest Point Computation}\par
The interest points are computed from generated integral image using pixel adjacency graph. For each pixel from the integral image we generate the pixel adjacency graph with the size from 3×3 to n×n to minimize the number of interest points. The overlapping interest points are dropped from execution and to reduce the execution time of the process. The interest points are computed with 64 features of the region identified. A point from the integral image will be selected based on the feature distribution around the pixel. From the constructed adjacency graph we select the pixel which has more features surrounded and will select the pixel to represent the region. The interest points are used to represent the region of an image even at different scaling and transformation or shifting. Step7 : Stop.\par
VI. 
\section[{Volumetric Estimation}]{Volumetric Estimation}\par
The volumetric measure of the image is computed based on the feature density measures i.e. how much the feature at a particular point is dense to represent the image region. For each integral image and interest points identified the selected pixel position is  
\section[{Probabilistic Classifier}]{Probabilistic Classifier}\par
With the computed set of interest points IpList, we compute the probability value for each class trained. End.\par
(Ips).\par
Step5 : Select the class with more probability.\par
Step6 : Assign label with the class. Step7 : Stop.\par
VIII. 
\section[{Results and Discussion}]{Results and Discussion}\par
The proposed probabilistic volumetric robust feature based classifier has produced efficient results than other classifier. We have evaluated the proposed algorithm with different methodologies discussed earlier.  
\section[{Conclusion}]{Conclusion}\par
We proposed a new probabilistic model to classify the color images using volumetric robust features, which uses intensity and color values to generate the interest points using which the probability value is computed. The computed probability value is used to classify the images. The proposed method has produced better results than other classifier with low time and space complexity. \begin{figure}[htbp]
\noindent\textbf{2013}\includegraphics[]{image-2.png}
\caption{\label{fig_0}I © 2013 F}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}}\end{figure}
    \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.85\textwidth}}
Step3 : Initialize probability set Ps.\\
Step4 : For each class available\\
Algorithm:\\
Step1 : Start\\
Step2 : Read interest points IpList.\end{longtable} \par
 
\caption{\label{tab_0}}\end{figure}
 			\footnote{© 2013 Global Journals Inc. (US)} 			\footnote{Probabilistic Color Image Classifier Based on Volumetric Robust Features} 		 		\backmatter  			  				\begin{bibitemlist}{1}
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\end{document}
