ANALYSIS ON CONTENT BASED IMAGE RETRIEVAL SYSTEM

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Available Online at www.ijpret.com 1581

INTERNATIONAL JOURNAL OF PURE AND

APPLIED RESEARCH IN ENGINEERING AND

TECHNOLOGY

A PATH FOR HORIZING YOUR INNOVATIVE WORK

ANALYSIS ON CONTENT BASED IMAGE RETRIEVAL SYSTEM

*PROF. A. S. CHHAJED1_{, ISHWAR KHARAT}2_{, AMOL BATOLE}2_{, KOMAL GADEKAR}2_,

SHUBHANGI GUJAR2

1.Head Of Department of Information Technology, Anuradha Engg College Chikhli, Sant Gadge Baba Amravati University Amravati (Maharashtra) India

2.Department Of Information Technology, Anuradha Engg College Chikhli, Sant Gadge Baba Amravati University Amravati (Maharashtra) India Accepted Date: 05/03/2015; Published Date: 01/05/2015

Abstract:In the present scenario image retrieval plays an essential role. In CBIR there are different techniques for retrieving the target or close Images. In first techniques, this paper tending towards some basics of a particular CBIR system with that it shows some basic features of any image, these are like shape, texture, color and shown different techniques to calculate them. In the second techniques. It present a contain-based image retrieval system with four different methods. First applied l1-norm minimization method. Here image is retrieved through similarity computation, the database images are matched according to their histograms method. Secondly applied Image Euclidean Distance finding method .In this method feature matrix is generated by extracting the feature vectors of image. And then applied two filters Median and Laplace filter in DCT domain to retrieve the image. It is demonstrated through the simulation work that the new similarity metric has the potential to perform well as compared to the metric based on the standard. And performance and preciseness is checked by comparing.

Keywords: Histograms, Content Based Image Retrieval (CBIR), L1-Normalization method, image Euclidean distance, Median filter, Laplace filter, DCT.

Corresponding Author: PROF. A. S. CHHAJED

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INTRODUCTION

Retrieval the image by using metadata which can be the addition of keywords, captioning, and some explanation of the images. By using metadata user need to putting metadata with every images which is very much costly, time consuming and also required hard work. There are two kinds of technologies, text-based image retrieval and content based image retrieval (CBIR). In the text-based approach, images are usually manually searched by text descriptors. Its greatest merit is that when images are recorded correctly, good search results can be achieved. This approach has some limitations. To overcome text-based image retrieval limitation the CBIR was introduced and has become the predominant technology. Content-based image retrieval (CBIR) is became a very attractive area for research in recent years. Content-based image retrieval (CBIR) is a system, in which retrieves visual-similar images from large image database based. As the images grow complex, retrieve the right images become a difficult problem. Recently, content-based image retrieval techniques have been introduced. These techniques are image retrieval methodologies using only the characteristic values of image content without any additional caption or text information. With the advancement of digital image capturing devices and low cost electronic memory, huge amounts of images are being created everyday in different areas. The need for the development of efficient and effective methodologies to manage large image databases for retrieval is urgent. In the past, many content based image retrieval (CBIR) techniques have been proposed.

The content base Image Retrieval can be achieved by applying different techniques. Here I have explain some basic techniques and the second technique which contain four method.

 L1 -norm minimization technique.

 Image Euclidean Distance.

 IR based on Median Filter in DCT domain.

 IR based on Laplace Filter in DCT domain.

METEDATA

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Available Online at www.ijpret.com 1583 for later access to the image. While metadata-based searching is the most prominent search technique used in the Internet (e.g. for example it is used by the Google search engine) due to its low computational cost, its many limitations led to the development of alternative methods of image searching and retrieval that would solve these limitations. The efficiency of metadata-based image retrieval equals the search efficiency in the underlying metadata database. Therefore, the second method for searching and retrieving images is built starting from the disadvantages of metadata-based search techniques: as databases become increasingly large, a few words describing the image are not sufficient to capture the entire contents of the image. Moreover, performing annotations involves (partially or totally) the human work, which is subjective in terms of image descriptions, but also time consuming [15].

I. BASIC TECHNIOQE OF CONTENT BASED IMAGE RETRIEVAL SYSTEM

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Fig.1. Content Based Image Retrieval System

The system represents this query image with a feature vector. The similarities between the feature vectors of the query example and those of the images in the feature database are then computed and ranked. Retrieval is computed by applying an indexing scheme to provide an efficient way of searching the image database. Finally, the system ranks the retrieval results and then returns the images that are most similar to the query images. For the given image database, features are extracted first from individual images. The features can be visual features like color, texture, shape, region or spatial features or some compressed domain features. The extracted features are described by feature vectors. These feature vectors are then stored to form image feature database. For a given query image, we similarly extract its features and form a feature vector. This feature vector is matched with the already stored vectors in image feature database. Sometimes dimensionality reduction techniques are employed to reduce the computations. The distance between the feature vector of the query image and those of the images in the database is then calculated. The distances are then stored in increasing order and retrieval is performed with the help of indexing scheme.

FEATURE EXTRACTION

SIMILARITY CHECKING

RETRIEVED IMAGES

QUERY IMAGE IMAGES IN

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II. L1-NORMALIZATION TECHNIQUE

The recently proposed sparsely promoting l1-norm minimization technique finds the sparsest solution of an under-determined system of linear equations. In the present paper, the l1-norm minimization technique as a similarity metric is used in image retrieval. a new image retrieval method is presented with the l1-norm minimization method as similarity metric pseudo code of the proposed method is given below:

• Input: Feature matrix Φ, feature vector y of search image.

L: Number of images to be retrieved. Set Φ1 = Φ.

• For each of i = 1, 2, . . . , L

Solve Y =ix0 + e with ‖‖ e ‖‖ ≤ Ԑ

Let j be such that |xj| ≥ |xl| ∀ l _= j.

Let Φi+1 be Φi without ith_column.

ith_{most relevant image of search image is the image Corresponding to column of Φi.}

• Output: L number of images of database that are similar to search image.

III. IMAGE EUCLIDEAN DISTANCE

A central problem in image recognition and computer vision is determining the distance between images. Considerable efforts have been made to define image distances that provide intuitively reasonable results. Among others, two representative measures are the tangent distance and the generalized Hausdorff distance [4]. Tangent distance is locally invariant with respect to some chosen transformations, and has been widely used in handwritten digit recognition. The generalized Hausdorff distance is not only robust to noise but also allows portions of one image to be compared with an commonly used due to its simplicity.

Let x, y be two M by N images,

X = (x1 ,x2 ,. . . .,xMN ) , Y = (y1 ,y2 ,. . . .,yMN )

Where xkn+1 , ykn+1 are the gray levels at location (k,l) . The Euclidean distance dE (x,y) is given by

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Available Online at www.ijpret.com 1586 Intuitive result. For simplicity, let the gray levels be one at the black pixels and zero elsewhere. Computing the Euclidean distances yields dE(a,b) = 54 and dE(a,b) = 49. The pair with more similarity has a larger Euclidean distance! This phenomenon is caused by the fact that the Euclidean distance defined in (1) does not take into account that x, y are images, xk , yk are gray levels on pixels. For images, there are spatial relationships between pixels. The traditional Euclidean distance is only a summation of the pixel-wise intensity differences, and consequently small deformation may result in a large Euclidean distance. The most important positive definite function, which we make use of to construct the metric coefficients, is the Gaussian function, written as

Gij = f ( │Pi - Pj│ ) = 1/2πσ2 . Exp [-│Pi - Pj│2 ∕ 2σ2 ] Where σ is the width parameter and will be set to be 1 in the rest of this paper for simplicity. We denote the induced IMED as IME d . Let two images be

X =(x 1 ,x 2 ,. . . . ,x n ) and Y =(y 1 ,y 2 ,. . . . ,y n ) then d IME (x,y) is given by

d2IME(x,y) =1/2π∑MNij-1 Exp{-│Pi - Pj│2/2}(xi –yi)(xj –yj)

Recomposing the image distances in Fig. 1 by (1) yields dIME (a,b)=23.3 and dIME (a,c)= 27.3. The new metric does provide intuitively reasonable results.

Fig.2. Proposed System

Input Image

Find Feature Matrix

Solve Y = iX0 + e

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IV. MEDIAN FILTER

The images are also consisted of some noise. To remove the noise and to get an enhanced image, the median filter is applied on the gray scale image. The median filter is based on the neighborhood operations. It consists of a window which is encompassed over the image to order (rank) the pixels in the image area and then replace the central pixel with the determined values. The median filter replaces the value of a pixel by the median of the gray levels in the neighborhood of that pixel . This filtered image is used for feature extraction.

V. LAPLACE FILTER

The Laplace filter is a linear filter. In this filter a window or mask with some values works with the values of the image pixels in the neighborhood. The values in the filter window are called the filter coefficients. The result of this filter is the sum of the products of the filter coefficients and the corresponding image pixel values.

Fig.3. The average precision of all the image categories using the Three filters method

The Laplacian filter looks like this:

1 1 1

1 8 1

1 1 1

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Available Online at www.ijpret.com 1588 values first increase, then decrease. This change reflects the change in curvature expected along a discontinuity between a bright object and a dark background. Now form a new image by subtracting the laplacian filtered image from the original image of moon.tif. How does the resultant image differ from the original? This combination of filtering operations is a commonly used technique and is referred to as an ’unsharp mask’. Why does it produce this effect? HINT: Think about what happens along an edge when you do the image subtraction[14].

DISCRETE COSINE TRANSFORM (DCT)

Discrete cosine transform is made up of cosine functions taken over half the interval and dividing this interval into N equal parts and sampling each function at the center of these parts [16], the DCT matrix is formed by arranging these sequences row wise.

Wavelets are mathematical functions that cut up the data or signal into different frequency components by providing a way to do a time frequency analysis. Analysis of the signals containing the discontinuities and sharp spikes is possible with help of wavelet transforms [17], [18], [19]. Kekre’s generalized algorithm which generates the wavelet from any orthogonal transform is used to generate DCT wavelet as DCT is an orthogonal transform [18], [20]. To take advantage of this property of wavelet, this paper has proposed a new algorithm to represent the feature vectors in the form of discrete cosine wavelet transform coefficients for the CBIR.

The DCT definition of 2D sequence of Length N is given in equation (1) using which the DCT matrix is generated [20] [21]. The generalized algorithm which can generate wavelet transform of size N2xN2 from any orthogonal transform of size NxN is applied to DCT matrix and DCT Wavelet is developed which satisfies the condition of orthogonal transforms given in equation (2). Once the Discrete Cosine Transform Wavelet is generated following steps are followed to form the feature vectors of the images.

VI. APPLICATION

1. Crime prevention: Automatic face recognition systems used by police forces.

2. Security Check: Finger print or retina scanning for access privileges.

3. Medical Diagnosis: Using CBIR in a medical database of medical images to aid diagnosis by identifying similar past cases.

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Available Online at www.ijpret.com 1589 5. Architectural and engineering design: Designer needs to be aware of previous designs, particularly if these can be adapted to the problem at hand. Hence the ability to search design archives for previous examples which are in some way similar, or meet specified suitability criteria, can be valuable.

VII. RELATED WORKS

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VIII. CONCLUSION

This paper has surveyed the essential concepts of content-based image retrieval systems. This survey attempts to introduce the theory and practical applications of CBIR techniques. In this paper a CBIR algorithm is proposed in which the statistical color features are extracted from the quantized histograms of enhanced grayscale filtered images in the DCT domain and used for the similar image retrieval. Only the DC and the first three AC coefficients with more significant energy; are selected in each DCT block to get the quantized histogram statistical color features. These features are extracted for the median, the median with edge extraction and the Laplace filtered images one by one. In the present work, a new similarity metric is proposed for CBIR in terms of the recent powerful l1-norm minimization technique, which is developed for solving the underdetermined System of linear equations. It is demonstrated through the simulation work that the new similarity metric has the potential to perform well as compared to the metric based on the standard Euclidean distance for the CBIR of texture images. From the simulation results we can conclude that with the median filter we can retrieve almost of all class images and more relevant to the query Image.

ACKNOWLEDGEMENT

We are thankful to our Guide Prof. Ajay S. Chajjed sir to help in making paper and also thankful to all other staff of I.T department.

REFERENCES

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Available Online at www.ijpret.com 1591 6. H. Nezamabadi-pour and S. Saryazdi, "Object based Image Indexing and Retrieval in DCT Domain using Clustering Techniques," presented at the Proceedings of World Academy of Science Engineering and Technology, 2005.

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