image retrieval based on color and texture features

7/29/2019 Image Retrieval based on color and texture features

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Image Retrieval based on color and texture

features

I. IntroductionMultimedia data such as image and video has been widely spreading on account of internet prevalent.

In the recent years, content based image retrieval (CBIR) has been an active area in image processing.

Content based image retrieval has many application areas such as architectural design, education

commerce, military, medical diagnosis, biomedicine and web image classification. Due to the increase ofdigital images and video on internet, results to traditional text based retrievals based on keyword are not

sufficient enough to resolve image retrieval. CBIR can greatly enhance the accuracy and efficiency of

retrieving and managing the data of image.

CBRI can greatly enhance the accuracy and efficiency of retrieving and managing the data of the

image.

CBRI manipulate on different principles from keyword indexing. Some commercial CBIR systems

are now available. The CBRI system like IBMs QBIC, based on distribution and characteristics of colo r,shape, texture, sketch and example to retrieve image. Chabot system employs text along with color

histogram and integrates a relational database to retrieve image. VisulSEEK system developed at

Colombia University for Telecommunication Research employ color percentage from color and spatial

layout region of color to retrieve image. The features of Content based image such as color, shape,

texture and outline are used for image retrieval. Among these features, color is an important feature in

CBIR, which is invariant on size, orientation and complexity. In this paper, we proposed the the novel

feature extraction technique using HSV colur instead of RGB color space for image retrieval.

I. HSV COLOUR SPACEThe HSV stands for the Hue, Saturation and Value, is sometimes s referred as HIS for hue, saturation

and intensity, or HSB for hue, saturation, and brightness provides the perception representation according


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with human visual feature. The HSV model, defines a color space in terms of three constituent

components: Hue, the color type range from 00 to 3600 relative to the red primary at 00, passing through

the green primary at 1200 and the blue primary at 2400, and the back to red at 3600. Saturation, the

vibrancy of color Range from 0 to 100% and occasionally is called the purity. Value, the brightness

of the color: Ranges from 0 to 100%. HSV is cylindrical geometries, with hue, their angular dimensions,

starting at the, in our system, we employ the HSV color space instead of RGB color space in two regions.

One is the lightness component is independent factor of images and second is the component of hue and

saturation are so closely link with the pattern of human visual perception.

To decrease the number of colors used in the retrieval, we quantize the number of colors into several

bins. J.R.Smith designs the scheme to quantize the color space into 166 colors. Li design the non-uniform

scheme to quantize into 72 colors. We propose the scheme to produce 15 non-uniform colors. The

formula the transfers from RGB to HSV is defined as below:

The R, G, B represent ref, green and blue components respectively with value between 0-225, where

H stands foe Hue, S stands for saturation, V stands for value. In order to obtain the value of H from 00

to

3600, the value of S and V from 0 to 1, we do execute the transforming calculation.

The proposed scheme scheme for HSV space contains three phases. First of all we resize all images to

reduce the size of the images and processing time. Secondly we convert each pixel of resized image to

quantized color code. Finally we compare the quantized color code between the query image and database

image. In conventional schemes, they extract the image feature vector from images employ descriptor like

color Histogram Intersection and Minkowski Metric (LM norm) to measure the similarity of image for

matching between a query image and image from database. Whwn matching processing is compared,

results are sorted in ascending order and retrieval image are presented Minkowski Metric equation has

extended in equation, where Histogram Intersection is defined in equation 5.

II. EDGE HISTOGRAM DESCRIPTORThe Edge Histogram Descriptor in MPEG7 represent local edge distribution in the image which obtained

by portioning the whole image into 16(4 x 4) sub images as shown in figure 1. Edges in all sub-images are

characterized into five types, four directional edges named vertical, horizontal,45degree and 135 degree

and one non-directional edge. To generate the histogram of each sub-image a total of 80 histogram bins

(16 x 5, 16-sub-images and five types of edges) as shown in Table 1. The Edge Histogram Descriptor

captures the spatial distribution edges. Each of 16 sub-images is divided into image blocks to obtain the

edge histogram. Each sub-image block treated as a 2x2 pixel image-block. We employ the filters for edge


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detection shown as figure 2 to compute corresponding edge intensity values of edge exceed a given

threshold, then the corresponding image block is considered to be an edge block.

Histogram bins Semantics

BinCounts[0] Vertical edge of sub-image at(0,0)

BinCounts[1] Horizontal edge of sub-image at (0,0)BinCounts[2] 45-degree edge of sub image at (0,0)

BinCounts[3] 135-degree edge of sub image at (0,0)

BinCounts[4] Non-directional edge of sub image at(0,0)




BinCounts[76] Horizontal edge of sub-image at (3,3)




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image retrieval based on color and texture features

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