Content Based image Retrieval using Error Diffusion Block ... fileconstructed from the typical block truncation coding (BTC) or halftoning-based BTC compressed data stream without

Content Based image Retrieval using Error Diffusion Block

Truncation coding Features with Relevance Feedback

Mr.Vipul Mahajan#1, Mrs.Alka Khade*2, #Computer Science Department, Computer Science Department, Mumbai University, Mumbai University

[email protected]

[email protected]

Abstract— A new concept to index colour images using the features extracted from the error diffusion Block truncation coding (EDBTC). The

EDBTC produces two colour quantizes and a bitmap Image, which is further, managed using vector quantization (VQ) to create the image

feature Descriptor. Herein two features are presented namely, colour histogram feature (CHF),bit Pattern histogram feature (BHF) to measure

the similarity between a query image and the Target image in database. The CHF and BHF are calculated from the VQ-indexed colour quantized

and VQ- indexed bitmap image, respectively. The distance calculated from CHF and BHF can be utilized to measure the similarity between two

images. User relevance feedback is used to filter result and gives more specific result

Keyword- Error diffusionBlock Truncation coding, Colour Histogram Feature, Bit Pattern Histogram Feature, Vector Quantization

I. INTRODUCTION

Content-based image retrieval (CBIR), also known as query by image content (QBIC) “Content-based” means that the

search examines the contents of the image comparatively than the metadata such as keywords, tags, or descriptions related with

the image. In this situation might refer to colours, shapes, textures, or any other information that can be derived from the image

itself. CBIR is needed because searches that depend on purely on metadata are dependent on explanation quality and completeness.

Having humans manually explain images by entering keywords or metadata in a large database can be time consuming and may

not capture the keywords wanted to describe the image.

These image retrieval systems involve two phases, indexing and searching, to retrieve a set of similar images from the database,

the indexing phase extracts the image features from all of the images in the database which is advanced stored in database as

feature vector. In the searching phase, the retrieval system originates the image features from an image submitted by a user (as

query image), which are later utilized for performing similarity matching on the feature vectors stored in the database. The image

retrieval system finally proceeds a set of images to the user with a specific similarity criterion, such as color similarity and texture

similarity. Error diffusion is a type of half toning in which the quantization residual is distributed to Neighbouring pixels that have

not yet been processed. Its main use is to convert a multi-level image into a binary image.

Block Truncation Coding, or BTC,[22] is a type of lousy image compression technique for greyscale images. It divides

the original images into blocks and then uses a quantiser to reduce the number of grey levels in each block at the same time as

maintaining the same mean and

Standard deviation. A pixel image is divided into blocks of classically 4x4 pixels. For each block the Mean and Standard

Deviation of the pixel values are calculated; these statistics generally change from block to block. The pixel values selected for

each restored, or new, block are chosen so that each block of the BTC compressed image will have (approximately) the same

mean and standard deviation as the corresponding block of the original image.

Many previous systems have been developed to improve the retrieval accuracy in the content-based image

retrieval (CBIR) system. One type of them is to pay image features derived from the compressed data stream [1]–[4].

G. Qiu, “Color image indexing using BTC,”2003,[1] In this Block Truncation Coding is done by using Color Image indexing. It

is classical method and Decoding procedure is absent.

M. R. Gahroudi and M. R. Sarshar, “Image retrieval based on texture and color method in BTC-VQ

compressed domain,”2007,[2] in this Image Retrieval is base on texture as well as color method,here BTC-VQ domain is used. In

this also classical method is used and blocking effect and Contour false problem is present.

F.-X. Yu, H. Luo, and Z.-M. Lu, “Colour image retrieval using pattern co-occurrence matrices based on BTC and VQ,”

2011,[3[ in this by using pattern co-occurrence matrices, color image get retrieved which is based on BTC and VQ. In this Feature

extraction of different segment of image become difficult.

S. Silakari, M. Motwani, and M. Maheshwari, “Color image clustering using block truncation algorithm,”2009,[4] in this color

image clustering is used with BTC algorithm.In this feature separation become difficult

JASC: Journal of Applied Science and Computations

Volume V, Issue XII, December/2018

ISSN NO: 1076-5131

Page No:2162

As opposite to the classical method that extracts an image descriptor from the original image, this retrieval arrangement directly

makes image features from the compressed stream without first performing the decoding process. This type of retrieval

purposes to reduce the time computation for feature extraction/generation since most of the multimedia images are already

converted to compressed area before they are r ecorded in any s torage devices. In [1]–[4] the image features are directly

constructed from the typical block truncation coding (BTC) or halftoning-based BTC compressed data stream without

performing the decoding procedure.

The concept of the BTC [9] E. J. Delp and O. R. Mitchell, “Image compression using block truncation

coding,” 1979, is to look for a simpleset of characteristic vectors to replace the original images. Specifically, the BTC

compresses an image into a new area by dividing the original image into multiple nonoverlapped image blocks, and each block is

then represent with two extreme quantizers (i.e., high and low mean values) and bitmap image. Two subimages constructed by

the two quantizes and the consistent bitmap image are produced at the end of BTC encoding stage, which are later

communicated into the decoder module through the transmitter. To generate the bitmap image, the BTC scheme performs

thresholding operation using the mean value of each image block such that a pixel value greater than the mean value is regarded as

1 (white pixel) and vice versa.

Y.-G. Wu and S.-C. Tai, “An efficient BTC image compression tech-nique,”1998,[10] some attempts have been

addressed to improve the BTC reconstructed image quality and compression ratio, and also to reduce the time computation

Some applications have been proposed in the literature triggered by the successfulness of EDBTC, such as image

watermarking [11], [13], [14], inverse halftoning [16], data hiding [17], image security [18], and halftone classification [15]. The

EDBTC scheme performs well in those areas with capable results, as described in [11]–[15], since it provides better reconstructed

image quality than that of the BTC scheme. the concept of the EDBTC compression is provided to the CBIR area, in

which the image feature descriptor is constructed from the EDBTC compressed data stream.

The descriptor is directly derived from EDBTC color quantizes and bitmap image in compressed area by involving the

vector quantization (VQ) for the indexing. The similarity standard between the query and target images is simply measured

using the EDBTC feature descriptor.

The contribution can be summarised are as follows 1) Corel 2000 image database is used 2) User relevance feedback is used

.

II. PROPOSED SYSTEM

Fig 1 shows Architecture of Image Retrieval system There are two parts of the system offline and online respectively In

offline part Image database compressed by EDBTC. In EDBTC compression color quantization and bitmap image quantization

involved. Color quantization further computed by color histogram and Bitmap image quantization further compute by pattern

histogram respectively In offline part Image database compressed by EDBTC. In EDBTC compression color quantization and

bitmap image quantization involved. Color quantization further computed by color histogram and Bitmap image quantization

further compute by pattern histogram respectively In offline part feature extraction occurred CHF & BHF of particular image

stored in image database With CHF and BHF.In online part user gives image query that query image compressed by EDBTC

Features get extracted from EDBTC compression & similarity compression feature extracted from Image database Image Retrived

by matching similarity compression further by using Relevance feedback Final Image get Retrieved

Fig. . 1: System Architecture



ISSN NO: 1076-5131

Page No:2163

III. EDBTC PROCESSING for COLOR IMAGE

Fig. . 2: Processing for color image

A. The EDBTC compresses an image in an effective way by including the error diffusion kernel to generate a bitmap image.

Simultaneously, it produces two extreme quantizes, specifically, minimum and maximum quantizes. The EDBTC system have a

great advantage in its low computational complexity in the bitmap image and two extreme quantizes group

B. The EDBTC bitmap image can be obtained by performing thresholding of the interbank average value with the error kernel. In a

block based process, the raster-scan path (from left to right and top to bottom) is applied to process each pixel

C. The EDBTC performs the thresholding operation by including the error kernel. We first need to compute the minimum,

maximum, and mean value of the interband average pixels

D. The bitmap image is made

E. The intermediate value is also generated at the same time with the bitmap image generation.

F. The residual quantization error of EDBTC can be calculated

G. The EDBTC thresholding process is performed in a successive way. One pixel is only processed once, and the residual

quantization error is diffused and gathered in to the neighboring unprocessed pixels.

H. The two extreme quantizes consist of RGB color information obtained by searching the minimum and maximum of value in an

image block for each RGB color space. Two EDBTC color quantizes are computed by looking for the minimum and maximum of

all image pixels in each image block.

IV. METHODOLOGY

I. The EDBTC compresses an image in an effective way by including the error diffusion kernel to generate a bitmap

image. Simultaneously, it produces two extreme quantizes, specifically, minimum and maximum quantizes. The EDBTC system

have a great advantage in its low computational complexity in the bitmap image and two extreme quantizes group, here Floyd

steinberg error kernel is used.[22]

II. The EDBTC bitmap image can be obtained by performing thresholding of the interbank average value with the error

kernel. In a block based process, the raster-scan path (from left to right and top to bottom) is applied to process each pixel in

a given image. Suppose that f (x , y) and f̄ (x , y) denote the original and interband average value, respectively. The interband

average value can be calculated as



ISSN NO: 1076-5131

Page No:2164

The fR(x, y), fG(x, y), and fB(x, y) denote the image pixels in the red, green, and blue (RGB) color

channels, respectively. The interband average image can be viewed as the grayscale type of a color image.

III. The EDBTC performs the thresholding operation by including the error kernel. We first need to compute the minimum,

maximum, and mean value of the interband average pixels as

∀∀∀∀x,y

∀∀∀∀x,y

IV. The bitmap image h(x, y) is made using the following rule:

V. The intermediate value o(x, y) is also generated at the same time with the bitmap image generation. The value o(x, y) can

be calculated as

(6)

VI. The residual quantization error of EDBTC can be calculated as

(7)

VII. The EDBTC thresholding process is performed in a successive way. One pixel is only processed once, and the residual

quantization error is diffused and gathered in to the neighboring unprocessed pixels. The value f̄ (x , y) of unprocessed yet pixel is

updated using the following strategy:

(8)

Where is the error kernel to diffuse the quantization residual into its neighboring pixels which have not yet been

processed in the EDBTC thresholding

VIII. The two extreme quantizes consist of RGB color information obtained by searching the minimum and maximum of value

in an image block for each RGB color space. Two EDBTC color quantizes are computed by looking for the minimum and

maximum of all image pixels in each image block as

∀∀∀∀x,y ∀∀∀∀x,y ∀∀∀∀x,y

∀∀∀∀x,y ∀∀∀∀x,y ∀∀∀∀x,y



ISSN NO: 1076-5131

Page No:2165

A. Vector Quantization

VQ compresses an image in lossy method based on block coding principle. It is a fixed-to-fixed length algorithm. The

VQ finds a codebook by iteratively partitioning a given source vector with its known statistical properties to produce the code

vectors with the smallest average distortion when a distortion measurement is given as,

Let C = {c1 , c2 , . . . ,cNc } be the color codebook generated using VQ consisting Nc code words. The VQ needs many

images involved as the training set. The vector ck contains RGB color (or grayscale) information which is identical to the two

EDBTC quantizes. Except for generating the color codebook, it is also required to construct the bit pattern codebook

B=(B1,B2….BNb) consisting of Nb binary code words. For generating the bit pattern codebook, bitmap images are required as

input training set in the VQ process.

B. Color Histogram Feature (CHF)

The CHF is derived from the two EDBTC color quantizes, while BHF is computed from EDBTC bitmap image. In this

paper, the CHFmin and CHFmax are developed from the color minimum and maximum quantizers, respectively. The CHFmin

and CHFmax capture color information from a given image. These features represent the combination of pixel brightness and

color distribution in an image.

C. Bit Pattern Histogram Feature (BHF)

Another feature generated from a VQ-indexed EDBTC data stream is the BHF. This feature captures the visual pattern,

edge, and textural information in an image. The BHF can be obtained by tabulating the occurrence of a specific bit pattern

codebook in an image

D. Image Retrieval with EDBTC Feature

The similarity distance computation is needed to measure the similarity degree between two images. The distance

plays the most important role in the CBIR system since the retrieval result is very sensitive with the chosen distance metric. The

image matching between two images can be performed by calculating the distance between the query image given by a user

against the target images in the database based on their corresponding features (CHF and BHF). After the similarity distance

calculation, the system returns a set of retrieved image ordered in ascending manner based on their their similarity distance

scores .

/

+ /

/

E. Relevance feedback

In this paper user relevance feedback is used, after retrieving images user need to select correct output image from

retrieved images list so after sending selective images, it gives final relevant images

IV Performance Measurement

An effectiveness of the proposed EDBTC retrieval system is measured with the precision, recall and average precision and recall

values rate values. These values indicate the percentage of relevant image retrieved a CBIR system after user relevance feedback

with a specific number of retrieved images L. The precision (P(q)) and recall (R(q)) values are defined as

(P(q)) =nq / L (12)

(P(q)) =nq / Nq (13)

Where nq and Nq denotes the number of relevant image against a query image q,and the number of all relevant images against a

query image q in database



ISSN NO: 1076-5131

Page No:2166

Fig. 3 Image sample from Corel 2000

V.EXPERIMENTAL RESULT

In this section we test 10 different images on the basis of six different features like CHFmin, CHF max,BHF,CHF min

+CHF max,CHF min +BHF ,CHF max + BHF and CHF min +CHF max +BHF. We record correct retrieved images out of 30

images, precision and recall and time complexity of each image with respect to different features.

Fig. 4 Graph (a) to (j) Precision vs Recall



ISSN NO: 1076-5131

Page No:2167

Above all graph from (a) to (j) shows Precision vs Recall for different 10 images separately .following table shows

average values of recall. Precision and time for ten images with six features and figure 5 shows graph of average values of

precision vs recall.

Table I Average values of Recall, Precision and Time for ten images with six features

FEATURES NAME RECALL PRECISION TIME COMPLEXITY

CHFMIN+CHFMAX+BHF 0.11 0.38 1 MIN 9 SEC

CHFMAX+BHF 0.10 0.34 1 MIN 35 SEC

CHF MIN+BHF 0.09 0.31 1 MIN 17 SEC

CHFMIN+CHFMAX 0.10 0.34 1 MIN 48 SEC

BHF 0.10 0.34 1 MIN 58 SEC

CHFMAX 0.11 0.35 2 MIN 15 SEC

CHFMIN 0.09 0.32 2 MIN 13 SEC

Fig. 5 Average of ten images- Precision vs Recall

Fig. 6 Steps of system

Figure 6 shows different steps of system fig (a) shows browsing image from database ,(b) shows EDBTC compression-

intermediate image , (c) shows EDBTC compression -bitmap image with error, (d) shows bitmap image, minimum quantization

and maximum quantization ,(e) shows features extraction of bitmap image, minimum quantization and maximum quantization ,(f)

shows retrieved images ,(g) shows user selecting relevant images ,(h) shows final images with respect to query images .



ISSN NO: 1076-5131

Page No:2168

VI. CONCLUSION

In Content Based image Retrieval using Error Diffusion Block Truncation coding Features with Relevance feedback,

Error Diffusion improves image quality comparative to only Block truncation coding. we use the Relevance feedback at the last

step so it will reduce unnecessary image which are not match with input image. So, by using feature and Relevance feedback we

will try to improve accuracy of retrieval system as well as quality of image. We test six features with respect to time

complexity ,precision and recall for ten images and finally collect average values of all ten images so it gives good result for

average values

ACKNOWLEDGMENT

For this paper my guide,my friends and colegues helps me.

I acknowledge Mrs.Alka Khade,mrs.Soorya Nair and other contributors for giving proper idea and directionto for making this

paper

REFERENCES

[1] G. Qiu, “Color image indexing using BTC,” IEEE Trans. Image Process., vol. 12, no. 1, pp. 93–101, Jan. 2003.

[2] M. R. Gahroudi and M. R. Sarshar, “Image retrieval based on textureand color method in BTC-VQ compressed domain,” in Proc. Int.

Symp. Signal Process. Appl., Feb. 2007, pp.1-4.

[3] ]. F.-X. Yu, H. Luo, and Z.-M. Lu, “Colour image retrieval using pattern co-occurrence matrices based on BTC and VQ,” Electron. Lett.,

vol. 47,no. 2, pp. 100–101, Jan. 2011

[4] S. Silakari, M. Motwani, and M. Maheshwari, “Color image clustering using block truncation algorithm,” Int. J. Comput. Sci. Issues, vol.

4, no. 2, 2009, pp. 31–35.

[5] Z.-M. Lu and H. Burkhardt, “Colour image retrieval based on DCT-domain vector quantisation index histograms,” Electron. Lett., vol.

41, no. 17, pp. 956–957, 2005.

[6] P. Poursistani, H. Nezamabadi-Pour, R. A. Moghadam, and M. Saeed, “Image indexing and retrieval in JPEG compressed

domain based on vector quantization,” Math. Comput. Model., vol. 57, nos. 5–6, pp. 1005–1017, 2013. [Online]. Available:

http://dx.doi.org/10.1016/j.mcm.2011.11.064

[7] M. E. ElAlami, “A novel image retrieval model based on the most relevant features,” Knowl.-Based Syst.,

vol. 24, no. 1, pp. 23–32, 2011.

[8] J.-M. Guo, H. Prasetyo, and H.-S. Su, “Image indexing using the color and bit pattern feature fusion,” J. Vis.

Commun. Image Represent., vol. 24, no. 8, pp. 1360–1379, 2013.

[9] E. J. Delp and O. R. Mitchell, “Image compression using block truncation coding,” IEEE Trans.

Commun., vol. 27, no. 9, pp. 1335–1342, Sep. 1979.

[10] ] Y.-G. Wu and S.-C. Tai, “An efficient BTC image compression tech-nique,” IEEE Trans. Consum. Electron., vol. 44, no. 2, pp. 317–

325, May 1998.

[11] J.-M. Guo and Y.-F. Liu, “Joint compression/watermarking scheme using majority-parity guidance and halftoning-

based block truncation coding,”IEEE Trans. Image Process., vol. 19, no. 8, pp. 2056–2069, Aug. 2010.

[12] J.-M. Guo, “Improved block truncation coding using modified error diffusion,” Electron. Lett., vol. 44, no. 7,

Mar. 2008, pp. 462–464.

[13] J.-M. Guo, S.-C. Pei, and H. Lee, “Watermarking in halftone images with parity-matched error diffusion,”

Signal Process., vol. 91, no. 1, pp. 126–135, 2011

[14] S.-C. Pei and J.-M. Guo, “Hybrid pixel-based data hiding and block-based watermarking for error-diffused

halftone images,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 8, pp. 867–884, Aug. 2003.

[15] Y. F. Liu, J. M. Guo, and J. D. Lee, “Halftone image classification using LMS algorithm and naive Bayes,” IEEE

Trans. Image Process., vol. 20, no. 10, pp. 2837–2847, Oct. 2011

[16] Y.-F. Liu, J.-M. Guo, and J.-D. Lee, “Inverse halftoning based on the Bayesian theorem,” IEEE Trans.

Image Process., vol. 20, no. 4, pp. 1077–1084, Apr. 2011.

[17] J.-M. Guo and Y.-F. Liu, “High capacity data hiding for error-diffused block truncation coding,” IEEE Trans.

Image Process., vol. 21, no. 12,pp. 4808–4818, Dec. 2012.

[18] ] J.-M. Guo and Y.-F. Liu, “Halftone-image security improving using overall minimal-error searching,”

IEEE Trans. Image Process., vol. 20, no. 10, pp. 2800–2812, Oct. 2011.

[19] Color Image Clustering using Block Truncation Algorithm IJCSI International Journal of Computer Science

Issues, Vol. 4, No. 2, 2009



ISSN NO: 1076-5131

Page No:2169

[20] HSV Color Motif Co-Occurrence Matrix for Content based Image Retrieval International Journal of Computer

Applications (0975 – 888) Volume 48– No.16, June 2012

[21] Enhancement Image Compression Using BTC Algorithm Volume 4, Issue 2, February 2014 ISSN: 2277 128X

[22] Vipul R. Mahajan and Alka Khade “A survey;:Content Based Image Retrieval using Block Truncaton Coding” Vol 7,No. 12 December 2017



ISSN NO: 1076-5131

Page No:2170



ISSN NO: 1076-5131

Page No:2171

Documents

Content Based image Retrieval using Error Diffusion Block ... fileconstructed from the typical block truncation coding (BTC) or halftoning-based BTC compressed data stream without