Abstract
This paper proposes a novel polynomial transform to modify the original histogram of the image to adjust the pixel density equally towards the high intensity levels so that uniform distribution of the pixels can be obtained and the image can be enhanced. We have shown the efficient use of this modified histogram for Content Based Image Retrieval. According to the CBIR system described in this paper each image is separated into R, G and B plane and for each plane a modified histogram is calculated. This modified histogram is partitioned into two parts by calculating the Center of gravity and using it 8 bins are formed on the basis of R, G and B values. These 8 bins are holding the count of pixels falling into particular range of intensity levels separated into two parts of the histogram. This count of pixels in 8 bins is used as feature vector of dimension 8 for comparison to facilitate the image retrieval process. Further these bins data is used to form the new variations of feature vectors ; Total (sum) and Mean of pixel intensities of all the pixels counted in each of the 8 bins. These feature vector variation has also produced good image retrieval. This paper compares the proposed system designed using the CG based partitioning of the original and histogram modified using the polynomial transform for formation of the 8 bins which are holding the Count of pixels and Total and Mean of intensities of these pixels. This CBIR system is tested using 200 query images from 20 different classes over database of 2000 BMP images. Query and database image feature vectors are compared using three similarity measures namely Euclidean distance, Cosine Correlation distance and Absolute distance. Performance of the system is evaluated using three parameters PRCP (Precision Recall Cross-over Point), LSRR (Length of String to Retrieve all Relevant images) and Longest String
Highlights
Content Based Image Retrieval is the promising approach to search the desired images from the large image databases using the image contents like color, shape, texture and their representation as feature vectors in various other formats [1][2][3]
Once the query image is fired it will proceed through all different stages shown in the figure, its feature will be extracted and will be compared with the database image feature vectors by means of similarity measure
Results after OR criterion are shown in Charts 3 for Total and Average of intensities Observing the chart 3 obtained for criterion OR over R, G B results of Total and Average of intensities, we can say that the PRCP is reached to good height that is from 0.35 to 0.5 for average results of 200 queries
Summary
Content Based Image Retrieval is the promising approach to search the desired images from the large image databases using the image contents like color, shape, texture and their representation as feature vectors in various other formats [1][2][3]. CBIR is one of the important area to be studied today to overcome the drawback of text based image retrieval techniques [6][7] It is vast area for researcher’s to find out new approaches to retrieve the similar images from database with very good accuracy and less computational complexity[8][9][10][11]. In our system we have solve this issue by exploring the new technique to form the bins so that color details of the image will be separated properly, feature vector size can be reduced and the comparison will take less time. To get the uniform pixel distribution we have used CG i.e Center of Gravity to divide the histogram in two equal parts This process is applied separately to each R, G and B plane of the image for extracting its features.
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