Applying Specific Region Frequency and Texture Features on Content-based Image Retrieval

Amin Abdullahzadeh Faculty of Electrical & Computer Engineering

University of Sistan and Baluchestan Zahedan, Iran

amin_abdz@pgs.usb.ac.ir

Farahnaz Mohanna Faculty of Electrical & Computer Engineering

University of Sistan and Baluchestan Zahedan, Iran

f_mohanna@ece.usb.ac.ir

Abstract—In this paper, a specific region called affine noisy invariant region is extracted from a query and database images to help accurate retrieval on different attacks. Then, only a 64××1 codebook based feature vector is obtained from this specific region applying vector quantization and codebook generation based on the Linde-Buzo-Gray algorithm, which reduces retrieval feature comparison calculations. Also a number of texture and frequency domain based features are computed and established for the region. Finally combination of these two groups of feature vectors improves the retrieval system efficiency. Besides, in order to optimize weighting combination coefficients of the feature vectors, the particle swarm optimization algorithm is applied. The experimental results show a real-time content-based image retrieval system with higher accuracy and acceptable retrieval time.

Keywords—content-based image retrieval; affine and noise invariant region; frequency domain based feature; texture based feature; particle swarm optimization

I. INTRODUCTION Recently, due to increasing digital images on the Internet,

content-based image retrieval (CBIR) system is the most efficient system in the image database retrieval [1-2]. The CBIR systems retrieve images by image characteristics. The CBIR techniques characterize images databases based on low-level features such as color, texture, shape and object [3-5]. Each type of these features has advantages and disadvantages [6]. However, humans use high level contents of the images [7]. The main problem in CBIR is the gap between low-level image features and high-level conceptual contents [3]. Therefore one of the most important issues in CBIR is how to extract good features in order to achieve more efficient retrieval results [8].

Color feature is invariant to image size and orientation [9]. Color histogram is a color feature that is often used in CBIR, but it does not include any spatial information [10-11]. Therefore different images will have similar or even same color histograms which perform more wrong CBIR retrieved results. Moreover, any changes in image colors by sharpening or blurring decrease the accuracy of the CBIR systems [12].

Another important visual feature is texture that refers to innate object surface properties and their relationship to the surrounding environment [13]. The conventional texture features used for CBIR are simultaneous auto-regressive (SAR)

model [14], edge histogram descriptor (EHD) [15], gray-level co-occurrence matrix (GLCM) [16], Wavelet moments [17], Markov random field (MRF) model [18] and Wold decomposition model [19]. Recently, two more texture descriptors; BDIP (block difference of inverse probabilities) and BVLC (block variation of local correlation coefficients) have been proposed. BDIP measures effectively local brightness variations on the image and BVLC measures local texture smoothness effectively [20].

One of the most challenging topics in image retrieval is that attacks such as; rotation, mirror reflecting, sharpening, blurring, noise and even their combinations cannot have any influences on CBIR system performances. Numbers of attacks are shown in Fig. 1. Therefore, in the CBIR system, in order to retrieve image databases accurately on attacks, an affine invariant region has to be selected from the images [21].

In this paper, according to [12], first an affine noisy invariant region (ANIR) of all the database images was segmented. Then, codebook based features of the ANIRs were extracted and indexed on the database. Thus for a query image, its ANIR was also segmented and its codebook based features were extracted as well. Finally by comparing query ANIR features with the ANIRs features of all the database images, retrieved results were reported.

Through the experiment on the ANIR, it is discovered that applying only a codebook including 64 codewords is good enough to have the same or even better accuracy in retrieval results as [12], faster and less computational. Also, in order to increase accuracy of the real-time CBIR system, for an ANIR, besides the codebook based features, a number of texture and frequency domain based features were computed and used as feature vectors. Furthermore, the particle swarm optimization (PSO) algorithm [22-25] is applied to optimize weighting coefficients of the features combination. Experimental results showed that applying texture and frequency domain based ANIRs feature vectors combined with a codebook based ANIR feature vector lead to a significant improvement in the CBIR system accuracy and performance.

The rest of the paper is organized as follow. Section II describes a summary overview on related works, including ANIR extraction, BDIP and BVLC algorithms for texture based feature extraction, and frequency domain based features extraction. Section III explains our proposed method.

2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP)

978-1-4673-6184-2/13/$31.00 ©2013 IEEE 289

Fig. 1. A number of different attacks including; 90-degree rotation, 180-degree rotation, 270-degree rotation, Salt and pepper noise adding, Blurring, Increasing brightness, Sharpening, Mirror reflecting, and Noisy 180-degree rotation from top-left to down-right respectively.

Experimental results and performance evaluation of the proposed method are shown in section IV. The Paper is concluded in section V.

II. RELATED WORK

A. Affine Noisy Invariant Region Extraction AIR which can defend against attacks was suggested by

Hung and Ho [21]. Therefore, AIR can be used as an invariable image property. Moreover, instead of applying the whole of the image in CBIR, only a specific region of an image, including unchangeable information on different attacks, will be employed in CBIR. Therefore, selection of such a region will help to retrieve with less computational and speed up CBIR system. As it is pointed in Fig. 1, an AIR is robust on salt and pepper noise attack, which is the worst image noise. Therefore in continues, the AIR is called an affine noisy invariant region (ANIR).A brief description of ANIR segmentation process is as below [21].

1- Use Gaussian Kernel filtering for the image according to (1).

(1)

where means standard deviation and f(x,y) is the image pixel at position (x, y).

2- Obtain local-based feature points based on (2).

MF(x , y) max {L(xt , yt, )} (2) xt , y t (Nb

2-1(L(x, y, ))∪ L(x, y, ))

where Nb2-1(L(x, y, )) showed b2-1 pixels of the area

from the center of L(x, y, ). The feature point set is represented by P, in (3).

P {(x,y) MF(x,y) L(x, y, ) } (3)

More robust feature points PA and PB in the set P can be calculated as (4) and (5).

PA max {sum(Nm× m (Pi ))} (4)

PB min {sum(Nm× m (Pi ))} (5)

Where Nm× m (Pi ) represents that feature point Pi is the center of block m × m.

3- Obtain center of mass (CI), applying (7).

The (p + q) geometric moment of M × N image f (x, y) is computed in (6).

mpq f (x, y) , p,q=1,2, …, n (6)

The central moment is defined as (7).

μpq f (x, y) ,

p,q=1,2, …, n , , (7)

where ( , ) shows the center of mass of f (x, y) and can also be called the CI which is an unchangeable characteristic.

4- Figure out the ANIR image.

Connecting CI, PA and PB, and extending to edge, and segmenting the image into four regions as Fig. 2(a), then, finding out the CI in each of the four regions as Fig. 2(b) and finally, connecting CIs of the four regions in order to get an AIR region of the image as Fig. 2(c).

5- Normalize the ANIR image.

Corners of the ANIR image, CI1, CI2, CI3, and CI4 are transformed to black square image corners (Fig. 3(a)) to obtain the normalized ANIR image which is shown in Fig. 3(b).

In order to retrieve images based on ANIRs in a CBIR system, the first step is extraction of AIRs from all the database images and a query. Extraction of the feature vectors from these ANIRs is next. For this purpose, the codebook generation based on the Linde-Buzo-Gray (LBG) algorithm [26], and vector quantization (VQ) technique [28] are used.

(a) (b) (c)

Fig. 2. The ANIR image extraction. (a) Connecting CI, PA, PB and segmenting the image into four regions, (b) Finding out the CI of each region, (c) Connecting the CIs to get an ANIR region.

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(a) (b)

Fig. 3. The ANIR image normalization. (a) Transition of the ANIR region corners to square image corners, (b) The normalized ANIR.

B. Codebook Based ANIRs Feature Vectors Extraction Applying Vector Quantization In part A, an ANIR image is extracted. In order to compute

codebook based ANIR feature vector, the VQ technique and the LBG codebook generation algorithm [27] are used to replace a 2D ANIR feature vector by a 1D vector. VQ maps m-dimensional Euclidean space Rm into a finite subset C of Rm. C is called a VQ codebook which includes a set of vectors called codewords [27]. Applying VQ, there is only an ANIR feature vector for each query and database images. Therefore s can be used to compare query with database images in retrieval. A brief explanation of the VQ technique and LBG codebook generation algorithm is as follow.

1- Define the codewords as square blocks of size 4 × 4 and divide the AIR image into non-overlapping square blocks with the same size as codewords. Each 4 × 4 divided block is called training vector.

2- Calculate a vector which is the average of all the training vectors. This vector is the codeword of the codebook#1.

3- Generate the next codebook as follow.

3-1- Split two initial vectors (IVs) from each codeword according to (8), (9).

IV1 = codeword + PV (8)

IV2 = codeword PV (9)

where PV is a randomly generated perturbation vector.

3-2- Find the closest IV for each training vector of the AIR image using the squared Euclidean distance.

3-3- Classify training vectors closest to the same IV, into the same cluster. Therefore, one cluster corresponds to each IV.

3-4- If a cluster has no member, its corresponding IV is replaced by a randomly selected training vector from the cluster with the largest number of training vectors and new clusters have to be formed again as in step 3-2 & 3-3, else continue.

3-5- After establishing all the clusters, replace the average of each cluster by the corresponding IV as new IVs.

3-6- Classify each training vector based on new IVs. If there is any change of clustering for any training

vector, go to step 3-4. Otherwise, new IVs are the codewords for new codebook.

4- Terminate the codebook generating process if the number of codewords is met the user pre-defined number such as 64 in our experiments. Otherwise, go to step 3.

Each codebook includes codewords. For example, a codebook#1 has 1 codeword, and a coodbook#4 has 8 codewords, and so on. Fig. 4, shows a codebook generated from extracted AIRs on randomly selected database images. After codebook generation, a feature vector for each normalized gray AIR can be extracted as

(10)

where, , (i=1, 2, …, N) is the number of blocks of a normalized gray AIR, that closest to the codeword.

C. Texture Based Feature Extraction Applying BDIP and BVLC Algorithms In order to extract texture based feature vector, the block

difference of inverse probabilities (BDIP) and block variation of local correlation coefficients (BVLC) in wavelet domain algorithms [20,28] are applied. Fig. 5 shows configuration of 2-level wavelet decomposition of an image. The BDIP extracts image edges and valleys as a texture feature according to (11).

(11)

where I(x,y) denotes the image intensity at pixcel (x,y) in the block of size (k+1) × (k+1), position index of the block , maximum distance of pairs of pixcels in the block, and block size according to .

The BVLC represents variation of block-based local correlation coefficients according to four orientations. Texture smoothness can be measured well applying the BVLC. Each local correlation coefficient is defined as local covariance normalized by local variance as shown in (12).

(12)

Fig. 4. A codebook generated from extracted ANIRs on randomly selected database images.

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Fig. 5. Configuration of 2-level wavelet decomposed of an image IC.

where and stands for the local mean and standard deviation of the block respectively. presents shift in one of the four orientations;

. and denote the local mean and standard deviation of the block which is shifted by , respectively. Then, the value of BVLC is computed as the difference between the maximum and minimum values of block-based local correlation coefficients based on four orientations applying (13).

(13)

According to (11) to (13), in the 1-level of wavelet sub-band image extraction, the BDIP and BVLC of each sub-band are computed. Next, the first and second moments of each sub-band BDIP and BVLC are calculated as components of a texture based feature vector, which are written in (14) to (17).

(14)

(15)

(16)

(17)

where shows the average.

Already, 16 feature components are extracted from four sub-bands of 1-level wavelet decomposition and 16 feature components from four sub-bands of in 2-level. Therefore a 32×1 texture based feature vector is generated combining these two 16 feature components groups applying (18).

,

(18)

Where and denote the mean and standard deviation vectors of the BDIP, respectively. Similarly,

and are the mean and standard deviation

Fig. 6. (a) An ANIR image, (b) Its BDIP output , (c) Its BVLC output

vectors of the BVLC, respectively. m is the sub-band orientation and n is the wavelet decomposition level. An ANIR image and its BDIP and BVLC outputs are illustrated in Fig. 6(a), 6(b) and 6(c) respectively.

III. PROPOSED METHOD First, an ANIR of all database images was extracted as II.A.

Next, 64×1 codebook based feature vectors of these ANIRs were computed as II.B. In order to improve the accuracy of the CBIR system, 32×1 texture based ANIRs feature vectors are calculated as II.C. Besides, 5×1 frequency based ANIRs feature vectors are extracted based on III.A. Furthermore, by combining these three feature vectors, a main feature vector of each ANIR database images are generated. Besides, the PSO algorithm is applied to optimize weighting coefficients of the feature vectors combination. Similar main feature vector is extracted from a query image as well. Finally by comparison of the query main feature vector with all the main feature vectors of database images according to ANMRR criterion, retrieved results were reported.

Experimental results showed that applying combined texture, frequency domain, and codebook based ANIRs feature vectors lead to a significant improvement in the CBIR system accuracy and performance.

A. Frequency Based Feature Extraction In order to extract frequency domain based feature vector,

The Fast Fourier Transform (FFT) [29] is applied according (19). The inverse FFT is calculated by (20).

(19)

(20)

where M and N are the image size. Fig. 7(a) and 7(b) illustrate an ANIR image and its FFT Absolut values;

respectively.

(a) (b) (c)

Fig. 7. (a) An ANIR image, (b) Its FFT output, (c) Its DCT output

h(y)

g(y)

h(y)

g(y)

2

2

2

2

2

2

IC (x, y)

g(x)

h(x)

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Another useful frequency domain transform is Discrete Cosine Transform (DCT) [30]. An advantage of DCT over FFT is have no complex computations. The DCT and inverse DCT are calculated by (21) and (22) respectively. Fig. 7(c) shows a DCT of an ANIR image.

(21)

(22)

where

(23)

The four corner components of FFT Absolut values and the

top left component of DCT value are considered as a 5×1 frequency domain based ANIR feature vector applying (24).

G = [ ] (24)

where is the absolute value of ANIR FFT at frequency component ; which M and N are image size. For example, denotes the absolute value of ANIR FFT at down left corner. is the top left ANIR DCT value.

B. CBIR Main Feature Vector Establishment In our proposed CBIR system, the main feature vector is

established applying (25).

(25)

where a1, a2 and a3 are weighting coefficients which are optimized by the PSO algorithm.

C. Proposed CBIR System Fig. 8 shows our proposed CBIR system as follow.

1) Extracting a 128 × 128 query ANIR image.

2) Extracting a 64 × 1 codebook based query ANIR feature vector applying the VQ technique.

3) Extracting a 32×1 texture based query ANIR feature vector applying the BDIP and BVLC.

4) Extracting a 5×1 query ANIR frequency domain based feature vector applying a number of the FFT and DCT component values.

5) Obtaining the main feature vector by combining three feature vectors calculated in step 2, 3, and 4 applying the PSO algorithm and creating a query index table (ITQ).

6) Comparing the ITQ with all ITIs contents individually. ITIs are created similar as ITQ for all database images. If the similarity is more than a threshold value TH, the query is similar to selected ITI. Otherwise, the comparison has to be continued to the next database image.

Fig. 8. The proposed CBIR system.

I is Retrieved

S < TH

S TH

S=Similar (ITI , ITQ)

For i=1,2,…,N

I++

Construct Index Table (ITQ)

Feature Database

(ITI)

Main Feature Vector ( )

64×1 codebook

based feature vector

32×1 texture based feature

vector

Query Image (Q)

Normalized ANIR Image

5×1 frequency

based feature vector

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IV. PERFORMANCE EVALUATION

A. Evaluation Criterion

Performance evaluation of the proposed method is calculated using Average Normalized Modified Retrieval Rate (ANMRR) criterion [31-32] which is defined by MPEG-7 group according to (26).

(26)

where NQ is the number of query images and NMRR measures retrieval performance for each query image according to (27).

(27)

where NG(q) is the size of ground truth set for a query q, Rank(k) is ranking of retrieved ground truth images and K(q) determines a relevance rank for each query. Since may have different sizes of ground truth images, a proper K(q) is calculated by (28).

(28)

where GTM is maximum of NG(q) over all queries. NMRR and ANMRR are in the range of [0, 1] and smaller values represent better retrieval performance.

B. Experimental Results In order to prepare a database for our experiments, Corel

database is selected. Corel database has 10 different image categories. However from these 10 categories, only one category including building images is chosen. Building category has 100 images. We have applied 90, 180 and 270 degrees rotations, mirror reflecting, Gaussian blurring, sharpening, salt and pepper noise adding, brightness changing and combination of 180 degree rotation plus salt and pepper noise adding to each of these 100 images. Thus our prepared database images include 1000 images. All of the experiments were also conducted with MATLAB 7.1 on Intel core-i5 CPU at 2.53 GHz and 4 GB memory.

Table I, shows that applying only a codebook including 64 codewords leads to the smaller ANMRR value and better CBIR performance. Table II, illustrates the performance evaluation results of the proposed CBIR system according to the ANMRR criterion and its retrieval time. These results include the proposed CBIR performance evaluation applying codebook based, texture based, frequency domain based and their combination ANIR features. The ANMRR is calculated for 40 query images.

TABLE I. SMALLER ANMRR APPLYING CODEBOOK INCLUDING 64 CODEWORDS

Number of codewords 8 16 32 64 128 256 512

ANMRR 0.342 0.179 0.130 0.113 0.118 0.118 0.124

TABLE II. PERFORMANCE EVALUATION RESULTS OF THE PROPOSED CBIR SYSTEM AND ITS RETRIEVAL TIME ACCORDING TO ANMRR CRITERION

BASED ON 40 QUERIES.

Method Codebook based

Texture based

Frequency domain based

Proposed

(combined)

ANMRR 0.1024 0.3158 0.4160 0.0905

Time (sec) 0.386 0.405 0.083 0.535

According to Table II, performance evaluation of the proposed CBIR system based on combined codebook, texture and frequency domain based ANIRs features presented 11.62% improvement in accuracy considering 0.149 sec increasing in retrieval time for each query. A number of retrieval results applying proposed CBIR system are demonstrated in Fig. 9.

Fig. 9. A number of proposed CBIR results

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V. CONCLUSIONS A CBIR system based on combined codebook, texture and

frequency domain ANIR features is proposed. It is discovered that only a 64 × 1 codebook based ANIR feature vector is good enough to have similar or even better retrieval results in comparison with larger codebooks such as 256 × 1 or 512 × 1. Furthermore, idea of combining BDIP and BVLC texture, frequency domain, and codebook based features on image specific regions applying the PSO algorithm, created a CBIR system more accurate than one using only codebook based features. However, the proposed CBIR system still has suitable retrieval time as well. According to the experimental results, proposed CBIR performance evaluation on prepared database based on the ANMRR criterion, presented 11.62% improvement in accuracy considering 0.149 sec increasing in retrieval time for each query.

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