Content-Based Image Retrieval Using Shifted Histogram

Yoo, Gi-Hyoung; Kim, Beob Kyun; You, Kang Soo

doi:10.1007/978-3-540-72588-6_142

Cited by 2 publications

(1 citation statement)

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“…Size of the feature vector decides the time required for comparing feature vectors for retrieval. It is an important factor to be considered while designing an efficient CBIR system [17][18] [19]. 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.…”

Section: Introductionmentioning

confidence: 99%

Bins Formation using CG based Partitioning of Histogram Modified Using Proposed Polynomial Transform ‘Y=2X-X2’for CBIR

Kekre¹,

Sonawane²

2012

IJACSA

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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.

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Section: Introductionmentioning

confidence: 99%