Colour Image Retrieval Based on Simplest Component Analysis (SCA) with RGB Components
M.Sumathi1*
Ph.D Research Scholar, Erode Arts and Science College, Erode-638 009, Tamilnadu, India.
Dr.S.Pannirselvam2
Associate Professor & Head, Department of Computer Science, Erode Arts and Science College,
Erode-638 009, Tamilnadu, India Dr.R.Sankarasubramanian
3Associate Professor in Computer Science, Erode Arts and Science College, Erode- 638 009, Tamilnadu, India.
Abstract
Today computers anchored its potential in various tasks of image processing. Due to its rapid growth various methods has been established to resolve different issues which ranges from the past to the present. The common task in image processing is the retrieval of images based on its contents. In earlier CBIR schemes major process has achieve by binary or gray scale images and such research steps nearer to its destination in terms of its accuracy. Hence, it motivates another channel to enter in the doors of colour image retrieval. It is now marching with various methodologies and shrinking with several issues. To overwhelm such challenges in this area of retrieval, we propose a novel image retrieval of color images based on its components using Principal Component Analysis (PCA) model. First, the input color image is separated its RGB component into each single component. Then using the PCA the image matching is accomplished with the one component process, then the two component process respectively. This proposed component based image retrieval provides better in many single components for matching..
Keywords: SCA,CBIR,RGB,Precision,Recall
1. Introduction
Generally, CBIR becomes a crucial image retrieval scheme in digital image processing in with image identification, recognition and restoration. In general image processing includes various tasks i.e image acquisition, image enhancement, normalization, segmentation, object recognition and image restoration etc., Each and every task occupies
its own positions and stands in its own research viz., enhancement, segmentation, retrieval.
The key component in image processing is the feature vectors which is extracted with various traditional methods as well as advances methods based on its growth. The features include the Texture, Edge, Shape, colour etc., and the semantic based features. During the past few decades the color based image retrieval explores it feather with various color features.
The application of image processing includes in various fields such as Medical sciences Space sciences, agriculture, and military; especially in security and protection systems.
Image retrieval using color feature is a complex task in various aspects. Generally, color image retrieval take place with the color spaces, HSV, Color corologram etc., retrieval of image based on Component is a challenging task, SCA is derived from PCA which is defined as an linear transformation that convert into coordinate transformation with
variance by the data projection form the initial coordinate , the second variance as second coordinate respectively.
The Simple component analysis (SCA) is a simple component based procedure that uses the major components and convert it into a particular class or set. These components are simply used for the other processing or transformations in which is a light weight process hence so called Simple component analysis. It can be used for coefficient transformation in image processing especially in color components based computations. It is a simplest method for component analysis and further classifications.
2. Related works
[1] In this method the HAAR wavelet is applied in the extraction of texture and color moments. [2] A new clustering method is used for image retrieval using image indexing, retrieval design and feature extraction for effective computational costs.
[3] A graph based method for image matching with the nodes which represents the region edges of the specified regions. [4] A survey on comparison of various image retrieval with the extraction of image features and its performance are compared to know the best methods.
[5] Different image retrieval methods using color features along with shape and wavelet transform using threshold.[6] A image retrieval using face features with the PCA and MLP. Noise removal is the primary task in this method and the face recognition take place.[7] A semantic based image retrieval is take place using the new framework with the relevance feedback on low level features..[8]New approaches on image processing using PCA which overcome form the environmental factors of gray scale images. [9]
Another method incorporates basic feature extraction with PCA. The basic feature extraction which includes color, shape and texture analysis aids in the recognition of basic elements in the image where pixels hold nonlinear relation. [10] Two conventional and widely used techniques known as (PCA) and discrete wavelet transform (DWT) are used for feature extraction.
[11] A new model using various descriptors which are obtained by signature and PCA based on the shaoe and objects [12] a new color model using PCA is developed in the classification of skin color with the RF and SVM classifiers.
[13] A new retrieval scheme for color based images with PCA in which the input image is convert to various possible color components and transform into uniform CIE LAB components.
3. Methodlogy
3.1 Image Retrieval Based on PCA
It is used in image processing specifically in image identification matching and the process. The process flow of image identification and retrieval based on principal component analysis. In this method the input image is fetched from the IDB and then the image features were mined using the PCA and stored in PCA feature database. Similarly, the image features for the images in the database are been extracted and stored in the image database. During the retrieval process the input image is considered to retrieve the target image. The input image is the feature database till the target image is obtained or the entire image in the feature database.
3.2 Proposed SCA model
This model is designed for the identification and retrieval for color images with the RGB primary tri-components of the image. The proposed model is shown as follows.
Split the RGB Components
Extract the Component features
Select the specified Component
Apply Correlation Function
Concatenate the related
components
Concatenate the related components
Display output Image
Fig.1 Proposed Process Flow Diagram
In this model image is selected from the color image database. Then bayers array is applied to separate the color components as different channels i.e RGB component is shown follows
Input Colour Image
Match Component
Based Future Data set
Fig.2 Bayer pattern
3.3 Correlation Function
After generating the color components, the future sets are generated and the correlation is made with the following equation.
0 2 0 2
0 0 1 1 1
ˆ ( )
2 2 2
R R R R
G R G G
where x and x are sample mean of two images 3.4 Interpolation of Missing RGB components
The re derive of the filter coefficients based on the basis of the following equation
0 2 0 2
0 0 1 1 1
ˆ ( )
2 2 2
R R R R
G R G G
Where the unknown values R1 and R-1 are valued as (R0 + R2) / 2 and (R0+R-2) / 2, respectively.
The interpolation is computed in the similar method.
Considering GHand GVare the interpolated green images. For each image, in every red or blue location, the chrominance values are calculated in a red pixel, and (or) in a blue pixel; name1
, ,
, ,
, ,
, ,
( , ) ( , )
( , ) ( , ) ( , )
( , )
i j Hi j
H H
i j i j
V
i j i j
V V
i j i j
R G if i j is a r e d lo c a tio n
C i j
B G if i j is a b lu e lo c a tio n R G if i j is a r e d lo c a tio n
C i j
B G if i j is a b lu e lo c a tio n
where i and j indicate the row and the column of the pixel ( i , j), 1 ≤ i ≤ M, 1 ≤ j ≤ N . Compute horizontal gradient for CHand the vertical one for CV
3.5 Interpolation R/B Values Missing
To interpolate the missing R and B values at a G sampling position as illustrated below.
The neighbouring green values
(
h v
g, g),( , ),( ,h v
gc gch v
gn gn),( , )h v
gs gs( , ) | ( , ) ( , 2 )|
( , ) | ( , ) ( 2 , )|
H H H
V V V
D i j C i j C i j
D i j C i j C i j
, ,
, , , ,
, ,
n g n g n
g g w c c g c g c
s g s g s
B h v
R h v G R h v
B h v
compute the reconstructed green values as follows
1 ( )
21 ( )
21 ( )
21 ( )
2
rc C w g w c g c
rc C w g w c g c
bc C n g n s g s
bc C n g n s g s
h G R h R h
v G R v R v
h G B h B h
v G B v B v
when estimating and . The vertical R interpolation has to use red samples of the horizontal neighbours RW, RC and ,
(1/2)( )
rc c w gw c gc
v G
R v R v
Consider the G sampling position with horizontal B and vertical R neighbours as follows:
1 ( )
21 ( )
1 (2 )
21 ( )
2
bc C w gw c gc
bc C w gw c gc
rc C n gn s gs
rc C n gn s gs
h G B h B h
v G B v B v
h G R h R h
v G R v R v
After the interpolation of green component due to is most dominance the other two components are interpolated.
3.6 Performance Metrics 1.Euclidean Distance
The Euclidean Distance
d x x
E
1 2, is estimated as follows
1 2
1
2
2, i n1
E i
d x x x i x i
wherex1(i) is the feature vector of input image i, and x2(i) is the feature vector of the target image i in the image database.
2.Precision (P) and Recall (R)
2
number of relevant images total number of relevant images in IDB R r
n
3.7 Proposed Algorithm Input: Input image
1
number of relevant images number of retrieved images P r
n
Output: Feature set.
Step 1 : Consider an input image (Ii) Step 2 : Partition the image Ii into different
Components Step 3 : Generate feature set
Step 4 : Accumulation the feature vectors into the feature set Step 5 : Step 1 through Step 7 is repeated for the
entire images
Step 6 : Match input image with the target image
Step 7 : Interpolate the remaining components Step 8 : Computer Euclidean Distance Step 9 : Resultant Image.
End.
4.Experiments and results
The proposed SCA model for color based image retrieval is experimented with the images collected from different databases of size (M × N).
The sample images are shown in the figure 3.
Fig.3 Sample texture images from database The following figure 4 shows the separated
RGB components of the sample image
With the algorithm as discussed in the above section the experimentation is take place and the image Flower2 is considered as target image and green component is applied for the correlation and the results are as follows
Color based target image
Fig.5 The Resultant Images
Table 1. Comparison table
The above table shows the performance evaluation.The precision and recall as described in the following table 1
Proposed SCA Model HSV YBCr
Recall Precision Recall Precision Recall Precision
0.82 0.68 0.62 0.44 0.61 0.48
Fig.6 The retrieval results obtained with the proposed method
5. Conclusion
A new component based retrieval approach based on color images are experimented with the images from the image database. In this proposed method the images get partitioned into the RGB components and the retrieval take place based on the components. The proposed model provided better result as represented in table 1.
References
1] KOMAL MIRCHANDAN, T.RAJANI MANGALA, “CONTENT BASED IMAGE RETRIEVAL”, International Journal of Industrial Electronics and Electrical Engineering, ISSN: 2347-6982, Volume-4, Issue-3, Mar.-2016
[2] Aasia Ali, Sanjay Sharma, “A Review: Content Based Image Retrieval Architecture and Technique”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Volume 6, Issue 9, September 2017, ISSN: 2278 – 1323
[3] AsmaNaz, Durgesh Warbude, Jaishree Boaddh,“Content Based Image Retrieval Based onYCbCr Approach”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Issue 2, February 2017, ISSN(Online): 2320- 9801
[4] Md. Shahadat Hossain and Md. Rafiqul Islam, “A New Approach of Content Based Image Retrieval Using Color and Texture Features”, British Journal of Applied Science & Technology, 21(3): 1-16, 2017;
ISSN: 2231-0843, NLM ID: 101664541
[5] Pradeep Kumar Kaushik, “Image Retrieval Based On Integration Between Histograms In Hsv Color Space With Texture Featur And Shape Feature’, International Journal of Scientific & Engineering Research Volume 8, Issue 10, October-2017, ISSN 2229-5518
[6] Anusree B S, Resmy V R, “Face Annotation and Similar Image Retrieval using PCA and MLP”, International Journal of Computer Science and Information Technologies, Vol. 6 (2) , 2015, 1300-1302,ISSN:0975-9646
[7] Sheeba Abraham, J. Bethanney Janney, SinduDivakaran, “Content Based Image Retrieval Using Principle Component Analysis”, International Journal of Computer Science And Technology Vol. 3, Issue 4, Oct - Dec 2012, ISSN : 0976-8491 (Online)
[8] K. Harshini, M. Narayana, “Content Based Image Retrieval Using Singular Value Decomposition”, International Journal of Engineering Innovation & Research, Volume 1, Issue 5, ISSN : 2277 – 5668
[9] Deepa Joseph1, Ajai M, “Content Based Image Retrieval of Corel Images Along With Face Recognition”, International Journal of Computer Science and Mobile Computing, IJCSMC, Vol. 4, Issue. 11, November 2015, pg.136 – 142,
[10] B. Kumar, ”Feature Extraction Using Principal Component Analysis and Discrete Wavelet Transform for Image Classification”, International Journal of Computer Sciences and Engineering, Vol.-6, Issue-8, Aug 2018,E-ISSN: 2347-2693
[11] G. Nagaveni, Dr. T. Sreenivasulu Reddy, “Detection of an Object by using Principal Component Analysis”, International Journal of Engineering Research &
Technology (IJERT), Vol. 3 Issue 1, January – 2014.
[12] Mahdi MaktabdarOghaz, MohdAizainiMaarof, AnazidaZainal, MohdFo’adRohaniS. HadiYaghoubyan, “A Colour Space for Skin Detection Using Principal ComponentsAnalysis Technique”, Journal of Applied Environmental and Biological Sciences, ISSN: 2090-4274.
[13] Ying Chen PengweiHao, “Optimal Transform in Perceptually Uniform Color Space and Its Application in Image Retrieval”,ICSP’04,0-7803-8406—7.
