Image Retrieval by Fusion of Color Histogram and Edge Orientation Histogram based on wavelet

Full text


Image Retrieval by Fusion of Color Histogram and Edge Orientation

Histogram based on wavelet

Devunoori Mounika & Mrs.B.Sreedevi

1PG Scholar, Department of ECE, Vaagdevi College of Engineering , Bollikunta, Warangal, Telangana 2Assosciate Professor, Department of ECE, Vaagdevi College of Engineering , Bollikunta, Warangal, Telangana ABSTRACT

It has very crucial realistic importance to research and research minority gown from the angle of pc imaginative and prescient for minority life-style protection and inheritance. As first exploration in minority get dressed image retrieval, this paper proposed a unique picture characteristic instance technique to offer a reason behind the rich records of minority get dressed photograph. Firstly, the color histogram and detail orientation histogram are calculated for divided sub-blocks of minority get dressed photo. Then, the final function vector for minority dress photo is typical with the resource of effective fusion of color histogram and detail orientation histogram. Finally, the improved Canberra distance is delivered to diploma the similarity amongst query photograph and retrieval photograph. We have evaluated the performances of the proposed algorithm on self-assemble minority robe photograph dataset, and the experimental consequences display that our Approach can efficiently specific the blanketed function of minority dress images, consisting of coloration, texture, shape and spatial facts.

Compared with some conventional techniques, our approach has higher and solid retrieval accuracy.



content cloth-primarily based absolutely actually snaps shots are a completely vital trouble depends in the recognition of patterns and artificial intelligence. Has been effectively carried out in lots of fields, alongside facet clinical analysis, textile enterprise and so forth. The minority style of the equal country has amazing personalities (uniform tone, style and styles.), Making it extra beneficial than ordinary natural pix in photo processing. Therefore, it is vital to investigate the scene capabilities of minority style. In this paper, the digital safety of country wide uniforms is studied from the perspective of computer imaginative and prescient. Although the countrywide minority garb photograph has complex visible capabilities, the primary tendencies are nonetheless the garb coloration, texture cloth and totem format, this is regular with the photo benefit inside the computer vision. So we're capable of use the extraction algorithms of the conventional function to extract the capabilities of minority gown images. At present, a big fashion of strategies have been advanced to extract color, texture and form features in advance, and already have first rate results in many areas. Color is the maximum dominant visible characteristic and

discrimination. The extraction strategies

encompass the cutting-edge-day color function color, coloration chart, straight away coloration,



Currently, the studies paintings of minority gown photograph retrieval remains in its infancy and exploration degree. We are the primary ones to behavior exploratory research at the retrieval era of minority dress photo. In this paper, we collect a minority dress photo dataset, in which some snap shots are curious about the aid of ourselves and a few are from the net. Most of the minority costumes in those photos are dressed thru minority human beings or human frame version, and some are snap shots of tiled minority costumes. Every ethnic corporation has its very own dress style, so we can distinguish among ethnic corporations by means of the usage of their costumes. After a series of researches on the traits of minority costumes in Yunnan, we choose the six maximum feature ethnic businesses‟ gown due to the fact the studies object, such as Bay nationality, Jingo nationality, Hani nationality, Miao nationality, Buoyed nationality and Val nationality. For every nationality we acquire a hundred robe pictures and preprocess them to length 128×ninety six or ninety six×128 in JPEG layout. Figure 1 indicates some photograph examples in the minority dress photo dataset. For now, no researcher has carried out exploratory studies on the retrieval generation of minority get dressed

picture. Nevertheless, many college students have researched on the image processing era of everyday clothing photo (specifically in e-commerce place) from the perspective of pc vision. Choy Yoo-Hoo [17] offers a unique method to retrieval the individual photograph that include the same clothing to a question photo from the picture set captured through using a couple of CCTV camera. Firstly, the garb region is discovered based on the placement of the face place; Then a characteristic vector is built for the apparel area, which composed through six coloration histograms of six sub-areas defined within the apparel vicinity. Wang Hay-extended [18] offers a manner of contour

characteristic extraction, expression and


image querying and retrieval gadget primarily based totally on color function for e-alternate clothing. The paper compares a selection of color function extraction techniques and similarity degree techniques. Experiments display that Euclidean metric and global shade histogram the use of RGB vicinity are pretty appropriate for clothing picture is seeking for. In big, all above algorithms are designed to give an explanation for the function of ordinary garb photograph from angle of color, shape, and texture.

Figure 1. An instance of the minority robe picture dataset


Color is an important seen feature for every human perception and laptop vision and it is widely utilized in image retrieval. The coloration

histogram is one of the most direct and the best shade function instance [21]. It has blessings of rework invariant, rotate invariant and scale invariant and has been extensively carried out in photograph retrieval. But it lacks spatial statistics. This paper includes spatial statistics to it with the aid of combining the color histograms for numerous sub-blocks defined inside the minority garb photograph. An appropriate shade vicinity and quantization must be precise alongside the histogram example. In this paper, three coloration regions (RGB, HSV and CIE L*a*b*) with incredible quantification variety are used to check the general ordinary performance of our Method. The experimental outcomes in Tables 1-3 demonstrate that the RGB color place with eight×four×4=128 quantification variety is the super preference in our framework. For an photograph with a length of M×N, we set the color quantification wide variety to L and denote the photo by manner of the equation x y x N y M

Edge Orientation Histogram


detection operator aerobe, Roberts, Prewitt and Canny. Sober is one of the most famous operator [22], that is referred to as after Irwin Sober and Gary Feldman. The Sober operator is based on convolving the photo with a small, separable, and integer valued clear out within the horizontal and vertical instructions and is consequently especially inexpensive in terms of computations. The operator uses threethree kernels which might be convolved with the unique picture to calculate approximations of the derivatives - one for horizontal changes, and one for vertical. If we outline R, G, B due to the fact the unit vectors along the R, G, B axes in RGB shade space




photograph retrieval which combines colour, texture, shape and spatial capabilities of minority dress photo efficiently. Our experimental outcomes display that our method has

appropriate retrieval average general

performance and sturdy adaptability. And it‟s an entire lot more effective than one-of-a-kind algorithms said in advance in the article, which encompass GLCM, LBP, LDP, Gabor-primarily based completely feature descriptor, Hu invariant distance, HOG, MTH, MSD and CDH. Because the nearby characteristic of minority robe image are apparent, area-based totally genuinely photograph retrieval for minority gown image dataset is probably studied in destiny paintings. Maybe, photograph segmentation may be considered as an assistant to extract the community function and semantic characteristic of minority gown picture.


[1] M. J. Swain, D. H. Ballard, Color Indexing, International Journal ofComputer Vision, Vol.7, No.1, 1991:11-32.

[2] M. Stricker, and M. Orengo, “Similarity of colour pictures”, In SPIE Conference on Storage and Retrieval for Image and Video Databases , quantity 2420, 1995, pp. 381-392, San Jose, USA.

[3] G. Pass, R. Zabin, J. Miller, Comparing Images Using Color Coherence Vectors, In ACM International Conference on Multimedia, Boston, MA, 1996: sixty 5-seventy three.

[4] J. HuangS. R. KumarM. MitraW. J. ZhuR.

Zabih, Image Indexing Using Color

Correlograms, Conference on Computer Vision & Pattern Recognition, 1997:762 – 768.

[5] OAB Penatti, RD Silva Torres. Color descriptors for internet image retrieval: a comparative test. The XXI Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI „08), Campo Grande, Brazil, 12–15 Oct. 2008: 163–one hundred seventy.

[6] R. Haralick, K. Shanmugam and I. Dinstein, Textural Features for Image Classification.IEEE Transactions on Systems, Man, and Cybernetics, VOL. SMC-3, No. 6, 1973(12).

[7] T. Ojala, M. Pietikäinen, T. Mäenpää, Multi-desire Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns, IEEE Transaction on Pattern Analysis and Machine Intelligence, 2002,24(7):971-987.

[8] T. Jabid, M. H. Kabir, O. Chae, Local Directional Pattern for Face Recognition,


Computing,Information and Control, Vol.Eight, No.Four, April 2012.

[9] Tai Sing Lee, Image Representation the usage of 2D Gabor Wavelets, IEEETransactions on pattern evaluation and gadget intelligence, VOL. 18, NO. 10, OCTOBER 1996.

[10] M. K. Hu, Visual pattern recognition via 2nd invariant, IEEE Transactions on Information Theory, 1962, 8(2):179-187.

[11] Eric Persoon, King-Sun Fu, Shape Discrimination Using Fourier Descrptors, IEEE Transactions on pattern assessment and machine intelligence, VOL. PAMI-8. NO. 3, MAY 1986.

[12] NavneetDalal, Bill Triggs, Histograms of Oriented Gradients for Human Detection, Proceedings of IEEE Computer Society Conference On Computer Vision and Pattern Recognition�IEEE Press, 2005: 886- 893.

[13] Guang-Hai Liu, Lei Zhang, Ying-Kun Hou, Zuo-Yong Li, Jing- YuYang, Image retrieval based totally on multi-texton histogram, Pattern Recognition, forty four(2011):2123-2133.

[14] Guang-Hai Liu, Zuo-Yong li, Lei Zhang, Yong Xu, Image retrieval primarily based mostly on micro-shape descriptor, Pattern Recognition, forty 3(2010):2380-2389.

[15] Guang-Hai Liu, Jing-YuYang, Content-based totally image retrieval usingcolor difference histogram, Pattern Recognition, 46 (2013):188–198.

[16] J. Z. Wang, J. Li, G. Wiederholdy,

SIMPLIcity: Semantics-sensitive Integrated

Matching for Picture Libraries, IEEE Trans. On Pattern Analysis and Machine Intelligence, vol.23, no.Nine, pp.947-963, 2001.

[17] Choi Yoo-Joo, Kim Ku-Jin, Nam Yunyoung, Retrieval of Identical Clothing Images based on Local Color Histograms, 1/3 International Conference on Convergence and Hybrid InformationTechnology, 2008, 1(1):818-823.

[18] Wang Hai-lengthy, Du Jun-li, Research on Clothing Image Retrieval Technology and System, 1st International Symposium on Computer Network and Multimedia Technology, 2009.

[19] Chen Jia-Lin, Chen Wan-Yu, Chen I-Kuei, Interactive Clothing Retrieval System, IEEE

International Conference on Consumer

Electronics (ICCE), 2014:349-350.

[20] Wang Yatong, Fu Wenlong, Wang Yongbin, Retrieval of Clothing Images Based on Color


Automation, Mechanical Control and Computational Engineering (AMCCE), region: Jinan, PEOPLES R CHINA, Date: APR 24-26, 2015, Advances in Intelligent Systems Research, Vol.124, p143-149,2015.

[21] Ju-xiang Zhou, Xiao-dong Liu, Tian-weiXu, Jian-hougan and WanquanLiu. A new fusion approach for content cloth based surely photograph retrieval with color histogram and nearby directional sample, Pattern Recognition Letters.

[22] I. Sobel, G. Feldman, "A 3x3 Isotropic Gradient Operator for Image Processing", supplied at the Stanford Artificial Intelligence Project (SAIL) in 1968.

[23] G. N. Lance, W. T. Williams,

Mixed-statistics classificatory applications I,





Download now (8 pages)