Jour of Adv Research in Dynamical & Control Systems, Vol. 10, 01-Special Issue, 2018
Local Extreme Edge Binary Patterns for Face Recognition and Image Retrieval G. Sucharitha, Department of Electronics and Communication, KL University, India. E-mail:
[email protected] Ranjan K. Senapati, Department of Electronics and Communication, KL University, India. E-mail:
[email protected]
Abstract--- In this paper, a new approach for image retrieval local extreme edge binary pattern(LEEBP) is proposed. The standard local binary pattern (LBP) encode the sign information of the local differences which are calculated between the center pixel and its neighbours. The proposed method differs from the existing LBP in such a way that it collects sign code using the magnitudes of local differences in all directions i.e. 00, 450, 900 and 1350. The local difference in each direction calculated between center pixel and its directional neighbours. Then, the binary pattern in respective direction generated based on the sign code magnitude of the local difference. Finally, LEEBP utilizes all four edges information in generating the binary code for each edge. Performance of the proposed method verified on three different databases and compared with LBP, Block based local binary pattern (Blk_LBP), Center symmetric local binary pattern (CS_LBP), Directional local extrema pattern (DLEP) and Local maximum edge binary pattern (LMEBP). The results showed an acceptable improvement in terms of their assessment measures as compared with the existing methods on respective databases. Keywords--- Local Extreme Edge Patterns, Local Quantized Patterns, Image Retrieval, Histogram, Corel-10K, A&T Face Database, Texture Database.
I.
Introduction
Retrieving the similar images for a particular query image from the database is called the content based image retrieval (CBIR). CBIR utilizes the low level features of an image like color, texture, shape and spatial layouts etc, in order to characterize and index the image. CBIR is used to reduce the semantic gap between low level features of an image and richness of human semantics. However, complete and extensive literature survey in [1-4]. Among all the low level image features, texture classification and extraction of features is an active research area. In Ref.[5] texture provides the important information in image classification as it illustrates the content of many real world images like bricks, fabrics, clouds, trees, leaves etc. Texture analysis is often concerned with detecting aspects of an image that are rotationally invariant and it has gained an extensive attention in the fields of face recognition, medical, image retrieval and object based image coding etc. Mean and variance of the wavelet coefficients used as texture features for image retrieval [6]. Gabor and discrete wavelet transforms are widely used for texture feature analysis [7-8]. Moghaddam et al. proposed the Gabor wavelet correlogram (GWC) for CBIR [9]. Ahmadian et al. used the wavelet transform for texture classification [10]. Ojala.et.al [11] proposed Local binary patterns (LBP) for extracting local information of each pixel using neighbouring pixels, in addition, LBP was converted in uniform and rotation invariant patterns [12]. Moreover LBP is used for facial expression analysis and recognition [13-14], object tracking [15], texture classification etc. Extension of LBP were introduced for better results. Heikkila et.al [16] proposed a modified version of LBP as the center-symmetric local binary pattern (CS-LBP) is a combination of LBP with scale invariant feature transform (SIFT) to describe the regions of interest. Completed LBP which is considering sign and magnitude in generating the binary pattern[17]. Dominant LBP [18], Line edge pattern for segmentation and image retrieval (LEPSEG & LEPINV) [19], local ternary pattern (LTP) [20], etc. have been proposed for image texture feature analysis and extraction. Qian et al.[21] et.al proposed pyramid LBP extracts multi resolution images based on the original image using a low pass filter and LBP. The homogeneity of the LBP is restricting to find the edge information of an image. To overcome this issue, Murala et.al [22] proposed directional local extrema patterns(DLEP) to extract the edge information in all possible directions and applied for CBIR. Hussain et.al [23] proposed the local quantized patterns for visual recognition. The block-based texture feature [24] which use the LBP texture feature as the source of image description is proposed for CBIR. Subrahmanyam et.al proposed various local patterns for texture feature extraction such as local tetra patterns (LTrP)[25], local maximum edge binary patterns(LMEBP)[26],local mesh patterns(LMeP)[27], directional binary wavelet patterns (DBWP)[28] and local ternary co-occurrence patterns (LTCoP) [29]. Manisha et.al[30] proposed a texture and color based image retrieval using LBP and Gray level co-
ISSN 1943-023X Received: 5 Dec 2017/Accepted: 15 Jan 2018
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Jour of Adv Research in Dynamical & Control Systems, Vol. 10, 01-Special Issue, 2018
occurrence. Vipparthi et.al[31] proposed a combination of LMEBP and magnitude local operator for image retrieval. The concepts of LQP[23] and LMEBP[26] have motivated to propose the local extreme edge binary patterns (LEEBP) for CBIR. 1.1. Main Contributions 1. The proposed method collects the extreme edge binary patterns (LEEBP) for the query and database images. 2. Edge binary patterns are calculated on taking the reference of LMEBP. 3. The proposed method tested face, corel-10k, STex databases. This paper is planned in the following style: In Section 1, introduction has been presented which comprises, inspiration, literature survey and main roles of the proposed method. Concise review of existing methods and proposed method explained in Section 2. In Section 3, demonstration and framework of proposed method has been discussed. Section 4 is experimental analysis and the proposed method validity. Finally, conclusion in Section 5.
II.
Review of Local Patterns
2.1. Local Binary Pattern LBP introduced by Ojala.et.al[11] for rotation invariant texture classification. Each pixel in the image considered as a centre pixel at a time. For a greyscale image,I of size mxn pixels and 𝐼𝐼(𝑔𝑔)denotes gray level of the 𝑔𝑔𝑡𝑡ℎ pixel in the image. A pixel at the centre becomes the threshold to derive the local binary pattern in a small 3x3 array of spatial structure. Mathematical expression for LBP is as given in Eq.(1)&(2). (1) 𝐿𝐿𝐿𝐿𝐿𝐿𝑃𝑃,𝑅𝑅 = ∑𝑃𝑃𝑖𝑖=1 2(𝑖𝑖−1) 𝑓𝑓(𝑔𝑔𝑝𝑝 − 𝑔𝑔𝑐𝑐 ) 1, 𝑔𝑔 ≥ 0 𝑓𝑓(𝑔𝑔) = � (2) 0, 𝑔𝑔 < 0 𝑐𝑐 𝑝𝑝 Where,𝑔𝑔 is gray value of centre pixel,𝑔𝑔 is gray value of circularly symmetric neighbourhood, P represents no. of neighbours and R is length of the neighbourhood. After deriving the LBP structure for the whole image, a histogram is built to represent the image as per the Eq(3)&(4). 𝑛𝑛 𝑃𝑃 𝐻𝐻𝐻𝐻𝐿𝐿𝐿𝐿𝐿𝐿 = ∑𝑚𝑚 𝑖𝑖=1 ∑𝑗𝑗 =1 𝐻𝐻1 (𝐿𝐿𝐿𝐿𝐿𝐿(𝑖𝑖, 𝑗𝑗), 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏); 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 ∈ [0, 2 − 1] (3) 1, 𝑢𝑢 = 𝑣𝑣 𝐻𝐻1 (𝑢𝑢, 𝑣𝑣) = � (4) 0, 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 Where the size of an image is 𝑚𝑚𝑚𝑚𝑚𝑚.
Fig.1 shows an example of calculating the LBP pattern for a3x3 matrix. The histogram of these patterns includes information on the distribution of edges in an image.
Figure 1: Calculation of LBP for a 3x3 Pattern 2.2. Local Maximum Edge Binary Patterns Subrahmanyam et.al[26] proposed this method for an image. The first maximum edge is attained by the magnitude of local differences between the center pixel and it’s all neighbours. After calculation of differences all the values are arranged in descending order using only magnitudes as shown in Fig.2.
Figure 2: Example for Local Maximum Edge Binary Pattern
ISSN 1943-023X Received: 5 Dec 2017/Accepted: 15 Jan 2018
645
Jour of Adv Research in Dynamical & Control Systems, Vol. 10, 01-Special Issue, 2018
Similarly, the remaining seven patterns are calculated to attain first maximum edge. The total eight maximum edges are evaluated using nine binary values. 2.3. Local Extreme Edge Binary Patterns The proposed LEEBP for a given image has four edges for each pixel. The four edges are calculated using the local differences between center pixel to its four directions individually i.e ± 00, ±450, ±900 and ±1350 as shown in the Fig.3. The four edges for each pixel will be calculated using the following Eq.s. The pixel for which edges are calculated assumed as 𝐼𝐼𝐼𝐼(𝑔𝑔𝑐𝑐 ) = 𝐼𝐼𝐼𝐼(𝑖𝑖, 𝑗𝑗) The 00 edge
𝐼𝐼𝐼𝐼(𝑖𝑖, 𝑗𝑗 − 1) − 𝐼𝐼𝐼𝐼(𝑔𝑔𝑐𝑐 ) ⎧ 𝐼𝐼𝐼𝐼(𝑖𝑖, 𝑗𝑗 − 2) − 𝐼𝐼𝐼𝐼(𝑔𝑔𝑐𝑐 ) (5) 𝑓𝑓(00 ) = 𝐼𝐼𝐼𝐼(𝑖𝑖, 𝑗𝑗 + 1) − 𝐼𝐼𝐼𝐼(𝑔𝑔𝑐𝑐 ) ⎨ ⎩𝐼𝐼𝐼𝐼(𝑖𝑖, 𝑗𝑗 + 2) − 𝐼𝐼𝐼𝐼(𝑔𝑔𝑐𝑐 ) The 450 edge 𝐼𝐼𝐼𝐼(𝑖𝑖 − 1, 𝑗𝑗 + 1) − 𝐼𝐼𝐼𝐼(𝑔𝑔𝑐𝑐 ) ⎧ 𝐼𝐼𝐼𝐼(𝑖𝑖 − 2, 𝑗𝑗 + 2) − 𝐼𝐼𝐼𝐼(𝑔𝑔𝑐𝑐 ) (6) 𝑓𝑓(450 ) = ⎨𝐼𝐼𝐼𝐼(𝑖𝑖 + 1, 𝑗𝑗 − 1) − 𝐼𝐼𝐼𝐼(𝑔𝑔𝑐𝑐 ) ⎩𝐼𝐼𝐼𝐼(𝑖𝑖 + 2, 𝑗𝑗 − 2) − 𝐼𝐼𝐼𝐼(𝑔𝑔𝑐𝑐 ) The 900 edge 𝐼𝐼𝐼𝐼(𝑖𝑖 − 2, 𝑗𝑗) − 𝐼𝐼𝐼𝐼(𝑔𝑔𝑐𝑐 ) ⎧ 𝐼𝐼𝐼𝐼(𝑖𝑖 − 1, 𝑗𝑗) − 𝐼𝐼𝐼𝐼(𝑔𝑔𝑐𝑐 ) (7) 𝑓𝑓(900 ) = ⎨𝐼𝐼𝐼𝐼(𝑖𝑖 + 2, 𝑗𝑗) − 𝐼𝐼𝐼𝐼(𝑔𝑔𝑐𝑐 ) ⎩𝐼𝐼𝐼𝐼(𝑖𝑖 + 1, 𝑗𝑗) − 𝐼𝐼𝐼𝐼(𝑔𝑔𝑐𝑐 ) The 1350 edge 𝐼𝐼𝐼𝐼(𝑖𝑖 − 1, 𝑗𝑗 − 1) − 𝐼𝐼𝐼𝐼(𝑔𝑔𝑐𝑐 ) ⎧ 𝐼𝐼𝐼𝐼(𝑖𝑖 − 2, 𝑗𝑗 − 2) − 𝐼𝐼𝐼𝐼(𝑔𝑔𝑐𝑐 ) (8) 𝑓𝑓(1350 ) = ⎨𝐼𝐼𝐼𝐼(𝑖𝑖 + 1, 𝑗𝑗 + 1) − 𝐼𝐼𝐼𝐼(𝑔𝑔𝑐𝑐 ) ⎩𝐼𝐼𝐼𝐼(𝑖𝑖 + 2, 𝑗𝑗 + 2) − 𝐼𝐼𝐼𝐼(𝑔𝑔𝑐𝑐 ) Sorting the magnitudes of each edge, after calculating the local differences in all directions using following Eq.s
𝑓𝑓(𝐼𝐼𝑂𝑂 0 ) = 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆(𝑚𝑚𝑚𝑚𝑚𝑚(|𝐼𝐼00 (1)|, |𝐼𝐼00 (2)|, |𝐼𝐼00 (3)|, |𝐼𝐼00 (4)|)) 𝑓𝑓(𝐼𝐼45 0 ) = 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆(𝑚𝑚𝑚𝑚𝑚𝑚(|𝐼𝐼45 0 (1)|, |𝐼𝐼45 0 (2)|, |𝐼𝐼45 0 (3)|, |𝐼𝐼45 0 (4)|)) f(I900 ) = Sort(𝑚𝑚𝑚𝑚𝑚𝑚(|𝐼𝐼900 (1)|, |𝐼𝐼900 (2)|, |𝐼𝐼900 (3)|, |𝐼𝐼900 (4)|)) 𝑓𝑓(𝐼𝐼135 0 ) = 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆(𝑚𝑚𝑚𝑚𝑚𝑚(|𝐼𝐼135 0 (1)|, |𝐼𝐼135 0 (2)|, |𝐼𝐼135 0 (3)|, |𝐼𝐼135 0 (4)|)) Assign the binary values to the edges as, ‘1’ if it is positive, otherwise ‘0’. 1 𝑓𝑓𝑓𝑓𝑓𝑓 ẍ ≥ 0 0 𝑓𝑓𝑓𝑓𝑓𝑓 ẍ < 0 Finally, the four edges are obtained from the following Eq.s 𝑓𝑓(ẍ) = �
(9) (10) (11) (12)
(13)
(14) 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿00 = 20 ∗ 𝑓𝑓�𝐼𝐼00 (1)� + 21 ∗ 𝑓𝑓�𝐼𝐼45 0 (1)� + 22 ∗ 𝑓𝑓�𝐼𝐼900 (1)� + 23 ∗ 𝑓𝑓�𝐼𝐼135 0 (1)� 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿45 0 = 20 ∗ 𝑓𝑓�𝐼𝐼00 (2)� + 21 ∗ 𝑓𝑓�𝐼𝐼45 0 (2)� + 22 ∗ 𝑓𝑓�𝐼𝐼900 (2)� + 23 ∗ 𝑓𝑓�𝐼𝐼135 0 (2)� (15) 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿900 = 20 ∗ 𝑓𝑓�𝐼𝐼00 (3)� + 21 ∗ 𝑓𝑓�𝐼𝐼45 0 (3)� + 22 ∗ 𝑓𝑓�𝐼𝐼900 (3)� + 23 ∗ 𝑓𝑓�𝐼𝐼135 0 (3)� (16) 0 1 2 3 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿135 0 = 2 ∗ 𝑓𝑓�𝐼𝐼00 (4)� + 2 ∗ 𝑓𝑓�𝐼𝐼45 0 (4)� + 2 ∗ 𝑓𝑓�𝐼𝐼900 (4)� + 2 ∗ 𝑓𝑓�𝐼𝐼135 0 (4)� (17) Individual histograms are constructed and concatenated to construct the feature vector after calculating the edges to each pixel. 𝑁𝑁 𝛼𝛼 (18) 𝑘𝑘 ∈ [0,15] α= 00, 450, 900 and 1350 𝐻𝐻𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝛼𝛼 (𝑘𝑘) = ∑𝑀𝑀 𝑖𝑖=1 ∑𝑗𝑗 =1 𝑓𝑓1(𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 (𝑖𝑖, 𝑗𝑗), 𝑘𝑘), 1 𝑢𝑢 = 𝑣𝑣 𝑓𝑓1(𝑢𝑢, 𝑣𝑣) = � (19) 0 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 The calculation of LEEBP for a centre pixel marked with red color is shown in Fig3. The local differences between the centre pixel and the directional pixels in horizontal, vertical, diagonal and anti-diagonal are calculated. Further, these local differences sorted in ascending order based on the magnitudes for all directions. The sorted local differences are coded '1' and '0' based on their sign. Finally, coding to 00 edge to the 1350 edge is performed using all direction edges (H, V, D, A).
ISSN 1943-023X Received: 5 Dec 2017/Accepted: 15 Jan 2018
646
Jour of Adv Research in Dynamical & Control Systems, Vol. 10, 01-Special Issue, 2018
The calculation and its result on a face is shown Fig.3 & Fig.4. The color face image is taken and the edges in all the directions are shown in Figure 4(a)-(e). All the four edges have the given the edge information in their respective direction. The 00 direction edge has more edge information as comparative to the 450, 900 and 1350.
Figure 3: Structure of Local Extreme Edge Binary Pattern
III. 1. 2. 3. 4. 5. 6. 7.
Proposed System Framework At first convert the RGB image into gray image. Compute the local difference between center pixel and its directional pixels in 00, 450, 900 and 1350 for each pixel in the image using Eq.s (5)-(12). Calculate the LEEP for each edge using Eq.s (13)-(17). Construct the histogram for each direction using Eq.(18)& (19). Concatenate all four histograms to construct the feature vector. Compare the database images with the query image using Eq.22 Retrieve the images based on the best matches.
Figure 4: Results of LEEBP on Face Feature Map. a) Sample Image b) 00 Direction Feature Map c) 450 Direction Feature Map d) 900 Direction Feature Map e) 1350 Direction Feature Map
ISSN 1943-023X Received: 5 Dec 2017/Accepted: 15 Jan 2018
647
Jour of Adv Research in Dynamical & Control Systems, Vol. 10, 01-Special Issue, 2018
Figure 5: Frame Work of the Proposed Method 3.1. Similarity Metrics for Query Matching Feature extraction has to be computed for all images including the query image, and a feature vector database has to construct for all the images in the database. Table 2 gives the length of feature vector for all methods. After completing the extraction procedure for the features of all images, similarity has to be calculated for query image to the database images. There are various distance measure metrics are available, some are given in terms of equations from Eq.20 -Eq.22. The proposed method d1 Eq.20 distance used to find the distance between query and database image feature vectors. 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓
𝑑𝑑1 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑: 𝑑𝑑(𝑞𝑞, 𝑏𝑏) = ∑𝑖𝑖=1 | 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶: 𝑑𝑑(𝑞𝑞, 𝑏𝑏) =
𝑓𝑓 𝑏𝑏 (𝑖𝑖)−𝑓𝑓 𝑞𝑞 (𝑖𝑖)
|
(20)
1+𝑓𝑓 𝑏𝑏 (𝑖𝑖)+𝑓𝑓𝑞𝑞 (𝑖𝑖) 𝑓𝑓 𝑏𝑏 (𝑖𝑖)−𝑓𝑓 𝑞𝑞 (𝑖𝑖) ∑𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑖𝑖=1 | 𝑓𝑓 (𝑖𝑖)+𝑓𝑓𝑞𝑞 (𝑖𝑖) | 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓
𝑏𝑏
(21)
𝑀𝑀𝑀𝑀𝑀𝑀ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎: 𝑑𝑑(𝑞𝑞, 𝑏𝑏) = ∑𝑖𝑖=1 |𝑓𝑓𝑏𝑏 (𝑖𝑖) − 𝑓𝑓𝑞𝑞(𝑖𝑖) | Where q is the query image, b is the database image
IV.
(22)
Experimental Results
To validate the performance of the proposed method, one face image database and two texture based image databases have been used. Each and every image of the database has taken a query image once for each database. The advantage of the proposed algorithm is confirmed based on evaluation measures i.e. recall and precision with some recent texture retrieval local patterns for image retrieval. The precision and recall are given in Eq.(23)-(24). Average precision and recall are defined as per Eq.(25)-(26). 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑛𝑛𝑛𝑛 .𝑜𝑜𝑜𝑜 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑡𝑡ℎ𝑒𝑒 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑
Precision(P)= Recall(R) = Avg.P =
1
𝑁𝑁 1
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑛𝑛𝑛𝑛 .𝑜𝑜𝑜𝑜 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 𝑡𝑡ℎ𝑒𝑒 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑛𝑛𝑛𝑛 .𝑜𝑜𝑜𝑜 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑡𝑡ℎ𝑒𝑒 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑
∑𝑁𝑁 𝑖𝑖=1 𝑃𝑃𝑖𝑖
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑛𝑛𝑛𝑛 .𝑜𝑜𝑜𝑜 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 𝑡𝑡ℎ𝑒𝑒 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑
∑𝑁𝑁 𝑅𝑅 𝑁𝑁 𝑖𝑖=1 𝑖𝑖
Avg.R = Where, N is the number of images in the database.
(23) (24) (25) (26)
Experiment 1 The Corel-10k [32]database is larger and adaptable than other Corel databases. It comprises of 10000 images of 100 different groups, where each group has 100 images. It includes images of animals, e.g fox, tiger, deer etc., human, natural scenes, ships, food, buses etc., army, ocean, cats, airplanes etc. Each image size of an image in the database is 85x128. Some of the sample images from the Corel-10k shown in Fig.6. The retrieved images for a query image are shown in Fig.7. In the Fig.7 top left image is the query image and the retrieved images according to their respective rank. Fig.8(a) and (b) shows performance of the proposed algorithm calculated in terms of precision and recall for each group in the database. Average precision and average recall are shown in Fig 8(c) and (d). Table 1: Results of Proposed Method and Previous Methods for Following Databases
CS_LBP Blk_LBP LBP DLEP LMEBP LEEBP
ISSN 1943-023X Received: 5 Dec 2017/Accepted: 15 Jan 2018
Corel-10K ARP ARR 27 10.3 38.5 14.5 38 14.3 40 14.9 39.5 15.1 47.3 17.7
S-Tex ARP 38 40 52 53.5 53 56
ARR 61 62.5 72 72.5 73.5 77
AT&T Face ARP ARR 90 45.7 89 44.9 85 42.1 92 50.7 94 51.8 97 56.5
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Experiment 2 The S-Tex image database (Salzburg texture) [33] has 7616 images of each image size is 128x128. The database has total 476 categories and each category has 16 image. Some of the sample images are shown in Fig.9.Different type of textures, e.g., rock, wood, water, rubber, food, etc. exist in this database. Each image in the database for every category has taken as a query image from the database of 7,616 images to evaluate effectiveness of the proposed method. Average precision and recall graphs have been shown in Fig. 10(a) and (b). The efficiency of the proposed method proved a major improvement in comparison with LBP, CS_LBP, Blk_LBP, DLEP and LMEBP.
Figure 6: Sample Images from Corel-10k Database Experiment 3 The AT&T is a face database[34]. It contains 40 different face images, for each face image 10 face images with various poses. The each image size in the database is 92x112. Some sample images from this database are shown in Fig.11. The first image in each face category has taken as a query image. For a query image the retrieved results are shown in Fig 12. The performance of the proposed method shown in Fig 13(a)&(b) in terms of precision and recall. The performance of the PM proved a significant improvement in comparison with LBP, CS_LBP, Blk_LBP, DLEP and LMEBP. Table1 explains the average precision rate(ARP) and average retrieval rate (ARR) of two different texture image databases and one AT&T face image database for other compared methods with the proposed algorithm. The ARR results are concluding clearly that the proposed algorithm do better than existing one.
Figure 7: Retrieved Results for a Query Image from Corel-10k Database
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CS_LBP
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Figure 8: Corel-10k comparative results a) Group Wise Precision b) Group Wise Recall c) Average Precision d) Average Recall
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CS_LBP
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Figure 9: Sample images from S-Tex database
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Figure 10: STex Database Results: (a) Average Precision (b) Average Retrieval
Figure 11: Sample Images from AT&T face Database Table 2: Feature Vector Length for a Given Query Image using Several Methods Method CS-LBP LBP DLEP LMEBP LEEBP
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Feature vector length 16 256 2048 4096 64
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CS_LBP
Blk_LBP
LBP
CS_LBP
Blk_LBP
LBP
DLEP
LMEBP
LEEBP
DLEP
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100 90 80 70 60 50 40 30
60 50 Recall
Precision
Figure 12: Results on AT&T Database
40 30 20 10
1 2 3 4 5 6 7 8 9 10
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Figure 13: Results of AT&T Database (a) Precision (b) Recall 4.1. Calculations Complexity Retrieval time of similar images from the dtabase is extremely dependent on the length of a feature vector. Generally distance metric will take more time for a lengthy feature vector in finding the difference between query image and database images. Comparision interms of the feature vector lengths in between proposed method to the further methods shown in table2 for speed evaluation. As per the table 2, the feature vector length of the proposed method is less than all the methods except CS_LBP only, even though the PM do better than other methods in terms of retrieval as stated in different database experimental sections. Also, DLEBP and LMEBP have lengthy feature vector .Hence, those algorithms are taking more time than the proposed method in extracting the related images to the query image.
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V.
Conclusion
A new image retrieval method has been proposed for various image databases. The idea to propose this method came from using local quantized pattern and local maximum edge binary pattern. The four edges for each pixel in all possible directions calculated with the help of sign code magnitude of local differences. By concatenating all the four directional histograms feature vector has been extracted. The proposed method tested on STEX texture database, Corel-10k database, AT&T face image database. Efficiency of the proposed algorithm demonstrated by successful experiments and provide evidence by comparing with other algorithms.
References [1] [2] [3] [4]
[5] [6] [7] [8]
[9] [10] [11] [12]
[13] [14] [15] [16] [17] [18] [19] [20] [21]
Wang, J.Z., Wiederhold, G., Firschein, O. and Wei, S.X. Content-based image indexing and searching using Daubechies' wavelets. International Journal on Digital Libraries 1 (4) (1998) 311-328. Rui, Y., Huang, T.S. and Chang, S.F. Image retrieval: Current techniques, promising directions, and open issues. Journal of visual communication and image representation 10 (1) (1999) 39-62. Long, F., Zhang, H. and Feng, D.D. Fundamentals of content-based image retrieval. Multimedia Information Retrieval and Management. Springer, Berlin, Heidelberg, 2003, 1-26. Smeulders, A.W., Worring, M., Santini, S., Gupta, A. and Jain, R. Content-based image retrieval at the end of the early years. IEEE Transactions on pattern analysis and machine intelligence 22 (12) (2000) 1349-1380. Liu, Y., Zhang, D., Lu, G. and Ma, W.Y. A survey of content-based image retrieval with high-level semantics. Pattern recognition 40 (1) (2007) 262-282. Smith, J.R. and Chang, S.F. Automated binary texture feature sets for image retrieval. IEEE International Conference on Acoustics, Speech, and Signal Processing, 1996, 2239-2242. Laine, A. and Fan, J. Texture classification by wavelet packet signatures. IEEE Transactions on pattern analysis and machine intelligence 15 (11) (1993) 1186-1191. Loupias, E. and Sebe, N. Wavelet-based salient points: Applications to image retrieval using color and texture features. International Conference on Advances in Visual Information Systems. Springer, Berlin, Heidelberg, 2000, 223-232. Moghaddam, H.A., Khajoie, T.T. and Rouhi, A.H. A new algorithm for image indexing and retrieval using wavelet correlogram. IEEE International Conference on Image Processing, 2003, 3-497. Ahmadian, A. and Mostafa, A. An efficient texture classification algorithm using Gabor wavelet. IEEE 25th Annual International Conference of the Engineering in Medicine and Biology Society, 2003, 930-933. Ojala, T., Pietikäinen, M. and Harwood, D. A comparative study of texture measures with classification based on featured distributions. Pattern recognition 29 (1) (1996) 51-59. Ojala, T., Pietikainen, M. and Maenpaa, T. Multi resolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on pattern analysis and machine intelligence 24 (7) (2002) 971-987. Ahonen, T., Hadid, A. and Pietikainen, M. Face description with local binary patterns: Application to face recognition. IEEE transactions on pattern analysis and machine intelligence 28 (12) (2006) 2037-2041. Shan, C., Gong, S. and McOwan, P.W. Facial expression recognition based on local binary patterns: A comprehensive study. Image and Vision Computing 27 (6) (2009) 803-816. Ning, J., Zhang, L., Zhang, D. and Wu, C. Robust object tracking using joint color-texture histogram. International Journal of Pattern Recognition and Artificial Intelligence 23 (7) (2009) 1245-1263. Heikkilä, M., Pietikäinen, M. and Schmid, C. Description of interest regions with local binary patterns. Pattern recognition 42 (3) (2009) 425-436. Guo, Z., Zhang, L. and Zhang, D. A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Processing 19 (6) (2010) 1657-1663. Liao, S., Law, M.W. and Chung, A.C. Dominant local binary patterns for texture classification. IEEE transactions on image processing 18 (5) (2009) 1107-1118. Yao, C.H. and Chen, S.Y. Retrieval of translated, rotated and scaled color textures. Pattern Recognition 36 (4) (2003) 913-929. Tan, X. and Triggs, B. Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE transactions on image processing 19 (6) (2010) 1635-1650. Qian, X., Hua, X.S., Chen, P. and Ke, L. PLBP: An effective local binary patterns texture descriptor with pyramid representation. Pattern Recognition 44 (10-11) (2011) 2502-2515.
ISSN 1943-023X Received: 5 Dec 2017/Accepted: 15 Jan 2018
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Jour of Adv Research in Dynamical & Control Systems, Vol. 10, 01-Special Issue, 2018
[22]
[23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34]
Murala, S., Maheshwari, R.P. and Balasubramanian, R. Directional local extrema patterns: a new descriptor for content based image retrieval. International journal of multimedia information retrieval 1 (3) (2012) 191-203. Ul Hussain, S. and Triggs, B. Visual recognition using local quantized patterns. Computer Vision–ECCV. Springer, Berlin, Heidelberg, 2012, 716-729. Takala, V., Ahonen, T. and Pietikäinen, M. Block-based methods for image retrieval using local binary patterns. Scandinavian Conference on Image Analysis. Springer, Berlin, Heidelberg, 2005, 882-891. Murala, S., Maheshwari, R.P. and Balasubramanian, R. Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Transactions on Image Processing 21 (5) (2012) 2874-2886. Subrahmanyam, M., Maheshwari, R.P. and Balasubramanian, R. Local maximum edge binary patterns: a new descriptor for image retrieval and object tracking. Signal Processing 92 (6) (2012) 1467-1479. Murala, S. and Wu, Q.J. Local mesh patterns versus local binary patterns: biomedical image indexing and retrieval. IEEE Journal of Biomedical and Health Informatics 18 (3) (2014) 929-938. Murala, S., Maheshwari, R.P. and Balasubramanian, R. Directional binary wavelet patterns for biomedical image indexing and retrieval. Journal of Medical Systems 36 (5) (2012) 2865-2879. Murala, S. and Wu, Q.J. Local ternary co-occurrence patterns: a new feature descriptor for MRI and CT image retrieval. Neurocomputing 119 (2013) 399-412. Verma, M., Raman, B. and Murala, S. Local extrema co-occurrence pattern for color and texture image retrieval. Neurocomputing 165 (2015) 255-269. Vipparthi, S.K. and Nagar, S.K. Local extreme complete trio pattern for multimedia image retrieval system. International Journal of Automation and Computing 13 (5) (2016) 457-467. Corel 10k Database, Available online:http://www.ci.gxnu.edu. in/cbir/. STex, Salzburg texture image database (STex), 2009. http: //wavelab.at/sources/STex/. AT&T Laboratories Cambridge, The AT&T Database of Faces, 2002. http://www.cl.cam.ac.uk/Research/DTG/attarchive/pub/data/att_faces.tar.Z
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