Adaptive Query Search Using CBIR

International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 5, Issue 6, June 2015)

Adaptive Query Search Using CBIR

Kanchan Vaishnav

, Dr. Sharvari. C. Tamane

1_{ME-Student, Department of Computer Science, Jawaharlal Nehru Engineering College, Aurangabad, India.} 2

Associate Professor, Department of Information Technology, Jawaharlal Nehru Engineering College, Aurangabad, India

Abstract—Image database holds variety and huge volume

of data due to emerging era of multimedia and communication. So, there is need to build a robust retrieval system for browsing whole database. The paper represents Adaptive Query Search approach for content-based image retrieval (CBIR), which depends on Support Vector Machines (SVMs). This method bridges the gap between low-level features and high-level semantics. The main goal of CBIR is to fetch visual content of an image like color, shape. We adopt HSV histogram, Autocorrelogram and color moments to accomplish color features, and shape feature are abstracted by Gabor wavelet and wavelet transform. Adaptive query search method adapts the search method according to the user’s interest residing on preceding retrieval result, using SVM classifier which permits the adapted retrieval approach to the user’s expectation much more in query images. SVM classifier ranks the most relevant images according to user expectations, so conclusion provoked using SVM method is more efficient.

Keywords—CBIR, Euclidean distance, SVM classifier,

Hamming distance, Similarity Measure

I. INTRODUCTION

Emerging era of computer technologies in combination with World-Wide Web and multimedia communication provides ease in development, storage, transmission, analysis, access of digital data. For the usage of this huge digital data, there is need of development of effective and efficient [1] database retrieval techniques. To escape from the defects such as excessive workload and strong subjectivity of traditional text-based image retrieval, Content-based Image Retrieval (CBIR) technology has emerged. Adaptive query search method in combination with SVM classifier is given which bridge the gap between low-level features and high-level semantics. Adaptive query search method provides a robust technique [2] to strengthen the system search effectively and efficiently. By making modifications in the query based on user’s feedback, proposed method boost the certainty of retrievals of content-based image retrieval, depending upon the situation where user can choose the most suitable images and supply appropriate weight preference for each suitable image. Retrieval is carried out by enabling the interaction between the system and the user to fulfill user’s intension, and to finally resolve the requests.

Retrieval process carried out by finding related images to the user's query [2], depending on selected features. For superior performance, adaptive query search technique associate features like color [3], [4] and shape with SVM classifier. HSV histogram [5], Autocorrelogram and color moments are used to accomplish color features, and shape feature [6], [7] are abstracted by Gabor wavelet and wavelet transform. For the purpose of measurement of similarity, we use Cityblock distance, Euclidean distance, Standard Euclidean distance, Hamming distance, and Spearman distance.

II. PRECISE CBIRBASED ON COLOR AND SHAPE FEATURE

A. Structure of Proposed CBIR:

The aspect of the existing research work is to provide retrieval of similar images from huge image database depending on query image provided by user. Architecture of proposed system is as shown in the figure 1 below.

Figure 1: CBIR Architecture

International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 5, Issue 6, June 2015) Its made up in 1990s and serves lots of work in

advancing area for researcher to make impressive development in system as well as theoretical [8] research.

CBIR adopts the visual contents like shape, spatial layout, texture and color of the images, for describing the image and for indexing the image. In CBIR technique, all visual contents [9] are abstracted and are illustrated by multi-dimensional feature vectors. Collection of all feature vectors of an image events into feature database. For recapture purpose, user acknowledges query image or example image, to CBIR system. Later on, system converts images submitted by user into its internal feature vector. The similarity orbit is determined between the images provided by user [10] i.e., query image and images those are present in the database with reference with indexing service. Indexing service contribute to effective and efficient to investigate image database.

Structure of proposed method is stated as below:

 Feature Extraction

Color Feature Extraction

Shape Feature Extraction

 Image Retrieval Process

 Similarity Measure

 SVM Classifier

III. PROPOSED ADAPTIVE QUERY SEARCH METHOD

WITH SVM

Relevance feedback method in CBIR is incapable to abstract high level semantics of images when only low-level features are used in RF. In many situations users are not aspire to review large number of feedback iterations to obtain superior result. In Relevance Feedback method user need to go through huge number of feedback iteration, which is time draining mechanism. Also practices of learning are online in RF scheme. Training and testing mechanism is compulsory for all feedback iteration. The procedure also requires enough real time. So, we required a robust system which bridges the gap between high level semantics [11] and low-level features and requires fewer real times. Proposed adaptive query search method delivers the functionalities which are lacks in RF technique [12]. It contributes flexibility to user to set the number of images to be retrieved from database.

Proposed scheme browses query image from selected folder. Depending upon the query image, system recaptures the most compatible images from classes of images, which are stocked into the database, conferring to user interest. User can classify the relevant and non-relevant images from produced set of retrievals for an image query.

Depending on the relevant and non-relevant images, similarity metric is corrected, reconstructed to figure out another set of recaptures. The objective of proposed adaptive search query system is to analyze the original interaction between user and retrival system, finding and capturing of user’s intension of search and then change the modification technique accordingly. When cycle of this process is completes, gained result matches with user’s actual demand. When we progress using SVM classifier, it ranks the images according to similarity [13] and retrieved the highly relevant images depending on ranks. Our system provides flexibility to user to set the number of images to be retrieved from database. With proposed adaptive query search scheme one can easily load dataset and create feature database of images. It fills the gap between high level semantics and low level features. Method puts the co-operation between high level semantics and low level features.

Support vector machines (SVM) are models of supervised learning which are collaborated with learning algorithms. Such algorithms are used to inspect data and observe patterns. If given a set of some query examples, SVM generates a model that distributes the query image into relevant category.

A support vector machine designs a set of hyperplanes or a single hyperplane in a high- or infinite-dimensional space. The use of such hyperplanes is for classification, regression, or other tasks. Hyperplanes are well known for good separation that has the largest distance to the nearest training-data point belonging to either class. It is real fact that, larger the margin, the lower the generalization error rate of the SVM classifier. The initial finite-dimensional space is to be mapped into a much higher-dimensional space, so that the partition becomes effortless in that specific space. To produced projected load as minimum as possible, the mappings concluded by SVM techniques are designed to assure that dot products may be calculated easily by using variables available in the original space and by characterizing them in terms of a kernel function k(x, y). The hyperplanes in the higher-dimensional space are determined as bunch of points whose dot product with a vector in that space is constant.

The hyperplanes are described as:

∑

α

k (x

, x) = Constant

Where, αi stands for parameters of images, x stands for test point and xi stands for data base point.

International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 5, Issue 6, June 2015) It results into a classifier having least Vupnik-

Chervonenkis (VC) dimension. The set of functions of VC dimension is the maximum number of points that can be separated in all possible ways by that bunch of functions. For hyper planes in R n, the VC dimension can be shown to be n + 1. SVM reduces the generalized error rate [13] on upper bound of image. Due to the moderate addition of training-error rate and a term that resides on the VC dimension, overall error rate can be easily restrained. Without any dilemma in domain knowledge, SVM is able to appear a good generalized performance.

A. Advantages of SVM:

1.By using the kernel, SVMs achieves flexibility in the threshold value which is responsible for the separating relevant and irrelevant images.

2.The kernel of SVM classifier includes some non-linear transformation, result of which is data becomes linearly separable.

3.If parameters are properly chosen, SVM yields into favorable generalization practices [14], which directed into robustness of SVM.

4.SVM classifier use convex optimality problem, so it provides a unique solution.

5.By selecting relevant kernel, we can put more stress on similarity between images, so that we can grab more convenient conclusion.

IV. SIMILARITY MEASURES

We have used Cityblock distance, Euclidean distance, Standard Euclidean distance, Spearman distance and Hamming distance to measure the similarity between the query image and images retrieved from the system. Each of them is explained below:

A. Cityblock Distance

City block distance is nothing but the summation of all distances along with its dimensions. The total City block distance between points a and b having dimensions k is computed as:

The city block distance between any two images is always greater than or equal to zero.

B. Euclidean Distance

Generally, to measure distance between two vectors, we use Euclidean distance and is illustrated by the square root of the sum of the squares of the differences between vector components. If given vectors A and B are as follows:

A = B =

The Euclidean distance is given by:

Where ai represents ith component in vector A and bi stands for ith component in vector B.

C. Spearman Distance

Spearman Rank Correlation is used to compute the correlation between two strings of values. The two strings are ranked individually and the variation in rank are computed at each position i. The distance between sequences X = (X1, X2, etc.) and Y = (Y1, Y2, etc.) is calculated using the following formula:

Where Xi and Yi are represents the ith values of strings X and Y respectively. Spearman Correlation is useful in identification of certain linear and non-linear correlations.

D. Hamming Distance

The Hamming Distance is a number used to represents the difference between two binary strings or image or vectors. It is a small portion of a broader set [15] of formulas used in information analysis.

Step 1

For calculating Hamming distance between binary images, bits of images must have equal length. Consider the example shown below. String 1 represents one image and String 2 another.

String 1: "1101 1001 0010" String 2: "1010 1100 0010".

b1

b2

.

.

b

International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 5, Issue 6, June 2015)

Step 2

Compare the first two bits in each string. If both bits are same, record a "0" for that bit. If both bits are different, record a "1" for that bit. As the first bit of both strings is "1," so record a "0" for the first bit.

Step 3

Compare each bit in progression and record either "1" or "0". String 1: "1101 1001 0010" String 2: "1010 1100 0010". After Comparision, the combination of two strings becomes: "0111 0101 0000"

Step 4

Add all the ones and zeros in the record together to obtain the Hamming distance. Hamming distance between above stings is = 0+1+1+1+0+1+0+1+0+0+0+0 = 5

V.RESULT AND ANALYSIS

We use HSV histogram, Autocorrelogram and color moments to fetch the color features of query image, with the guidance and services of Gabor wavelet and wavelet transform, and we have fetched shape information [16] contained in a query image. The proposed adaptive query search method takes the query image according to user interest for the purpose of boosting the retrieval outcomes. It adapts the search method according to the user’s interest residing on preceding retrieval result, using SVM classifier which permits the adapted retrieval approach to the user’s expectation much more in query images. SVM classifier ranks the most relevant images according to user expectations, so conclusion provoked using SVM method is more efficient. We carried out same process with database images. We have taken 10 different classes of images to perform retrieval of images as shown in table 1. Table below shows similarity measure of the Cityblock distance, Euclidean distance, hamming distance, Spearman Distance of images of 10 different classes. Figure 2 and 3 shows the top 20 outcomes of Horse image query and Dinosaurs image query.

Fig 2: Shows Top 20 retrieved images based on CBIR of horse as image query

International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 5, Issue 6, June 2015)

Table I.

Calculation of similarity measures

Sr.No. Similarity Measure Classes

Cityblock

Distance (%)

Euclidean Distance (%)

Std. Euclidean Distance (%)

Spearman Distance (%)

Hamming Distance (%) 1 1 Africa 79.80 77.20 80.40 80.60 84.20

2 79.40 79.80 80.80 82.40 80.60

3 82.80 80.80 79.00 82.80 79.80

4 80.60 82.40 81.40 78.80 82.00

5 83.20 80.80 81.40 81.00 79.40

1 2 Beach 82.40 81.00 82.40 78.00 77.40

2 80.80 82.00 80.80 79.60 84.00

3 79.60 81.00 82.40 80.60 81.80

4 80.80 78.80 81.20 78.60 80.20

5 78.40 80.20 80.40 80.20 82.60

1 3 Monuments 82.20 78.40 81.00 82.40 80.00

2 81.00 79.80 77.20 80.20 77.80

3 80.00 78.40 80.80 78.60 80.80

4 81.60 82.40 82.20 81.00 81.60

5 79.40 82.20 81.40 83.80 77.80

1 4 Buses 82.60 79.60 79.40 82.60 83.20

2 81.60 79.80 79.20 80.80 81.40

3 81.60 81.60 79.40 79.00 82.40

4 81.00 82.60 80.00 81.80 82.00

5 84.20 80.40 82.80 81.20 81.00

1 5 Dinosaurs 80.40 81.80 78.40 81.80 82.80

2 81.60 80.00 79.20 82.20 81.00

3 78.60 78.80 80.00 80.60 82.00

4 81.60 80.80 82.40 80.60 79.80

5 78.20 80.80 80.20 78.80 81.00

1 6 Elephants 80.20 82.40 80.40 80.20 82.40

2 79.00 81.00 81.80 80.20 80.80

3 80.60 83.20 82.60 83.00 78.20

4 79.20 79.20 83.00 80.80 79.20

5 83.80 79.00 79.00 79.00 82.60

1 7 Flowers 82.20 83.40 80.80 79.00 80.40

2 80.20 78.80 80.80 79.80 80.40

3 82.00 80.20 82.40 80.40 80.40

4 82.00 79.40 81.20 79.20 81.20

5 80.20 81.00 83.00 82.20 81.60

1 8 Horses 82.00 82.40 81.40 81.20 78.60

2 83.00 81.80 80.00 82.00 82.60

3 78.00 81.40 79.80 78.80 82.20

4 81.60 82.00 79.40 78.20 82.00

5 80.20 83.80 82.80 83.00 83.00

International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 5, Issue 6, June 2015)

2 80.40 79.40 83.00 80.60 79.40

3 81.40 79.20 82.80 83.00 82.60

4 78.80 83.20 82.80 81.80 83.80

5 80.00 81.40 82.40 78.40 80.80

1 10 Food 81.40 81.20 82.60 78.20 83.40

2 81.00 82.00 81.20 82.60 81.40

3 81.00 81.60 82.40 79.20 81.20

4 80.80 79.00 79.00 79.80 83.20

5 80.80 82.00 81.80 79.20 83.20

VI. CONCLUSION

In this adaptive query search method, SVM classifiers are used to distinguish between the classes of relevant and irrelevant images. SVM classifier creates the matrix to compute the similarity measurement. For calculations of similarity between retrieved image and query image, we used Euclidean distance, Cityblock distance, Spearman distance and Hamming distance measurement technique. Calculations are carried out on images of 10 different classes. Proposed adaptive query search method enhances the retrival results of images by using SVM classifier. The results shown exhibits the excellence of the proposed adaptive query search method as compared to other techniques based on feature selection methodology. Among other different methods of query image search method, SVM is efficient as it delivers risk minimization structure. Use of SVM is dynamic and generates outcomes which are more adequate according to user perception. SVM classification can be even better if the feature vector used in more relevant to images.

Acknowledgment

First and foremost, I would like to thank my guide, Dr. S .C. Tamane, for her guidance and support. I will forever remain grateful for the constant support and guidance extended by guide, in making this paper. Through our many discussions, she helped me to form and solidify ideas. The valuable discussions I had with her, the penetrating questions she has put to me and the constant motivation, has all led to the development of this project.

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