An Extension to the Local Binary Patterns for Image Retrieval

Transcription

1 , pp An Extension to the Local Binay Pattens fo Image Retieval Zhize Wu, Yu Xia, Shouhong Wan School of Compute Science an Technology, Univesity of Science an Technology of China, , Hefei, China Abstact. In this pape, we popose an extension to the local binay pattens fo image etieval. We focus on the spatial istibution infomation in images, an pesent a spatial-istibution-infomation-enhance local patten fo content-base image etieval. Diffeing fom taitional local pattens, we goup those gay-level vaying values accoing to thee iections, an each goup is then mege into a local spatial istibution patten to epesent the spatial istibution popety of the image. Ou peliminay expeimental esults on a eal ataset emonstate the effectiveness of ou algoithm. Keywos: Content-base Image Retieval; Local Pattens 1 Intouction Image etieval is an impotant fiel in image pocessing an patten ecognition, which is also an impotant banch of infomation etieval. Geneally, thee ae thee categoies of etieval methos: text-base, semantic-base, an content-base methos. The text-base metho was popose in 1970s an is still the most wiely use one in Web image etieval [1], howeve, the text-base metho heavily epens on human peception, as the images nee to be fist manually annotate using text tags, categoies, o othe infomation [2]. The semantic-base appoaches focus on eucing the semantic gap between the visual featues an human peception. People ten to use high-level featues to unestan images an measue thei similaity, but image low-level featues such as colo, textue, an shape often fail to escibe the high level semantic concepts [3, 4]. The content-base image etieval (CBIR) metho aims to oganize igital images accoing to thei visual featues, such as colo, textue, shape, istibution layout, an so on [5]. CBIR nees neithe manual image annotation no semantic labeling, an can ank the etieve images with espect to thei similaity with the quey image. Theefoe, it has been one of the hottest eseach topics in ecent yeas. The key issue in CBIR is to fin meaningful featues to epesent images. One of the most wiely-use an impotant featues is the textue of image. Textue ISSN: ASTL Copyight 2014 SERSC

2 featue is able to eflect a lot of popeties of image, such as smoothness, coaseness, an egulaity. Theefoe, many appoaches have been popose to escibe textue featues, among which the most influential one is the local patten base methos. A local patten of image efes to the gay-level iffeences between the efeence pixel of an image an its suouning pixels. Thee have been some iffeent local pattens pesente in ecent yeas, such as local binay pattens (LBP) an local tenay pattens (LTP), howeve, one common poblem of those pevious local pattens is that, they all ignoe the spatial istibution popety of textue in images. This will lea to the loss of useful textue infomation of image etieval in many cases, an ou expeimental esults in this pape will emonstate this claim. In this pape, we aim at incopoating the spatial istibution popety of image with the taitional local patten base CBIR methos, an pesent a new spatial-istibution-enhance local patten fo textue featues, which is calle local spatial istibution patten (LSDP). Compae with vaious taitional local pattens, LSDP eoganizes the gay-level vaying values among pixels in accoance with thee iections (hoizontal, vetical, an iagonal) so that it can epesent the textue featue of an image in iffeent spatial istibution pattens. The emaine of the pape is stuctue as follows. Section 2 intouces the local spatial istibution patten. In Section 3, expeiments an compaison between ou algoithm an pevious ones ae given. An the conclusions as well as the futue wok ae in Section 4. 2 Local Spatial Distibution Patten Taitional local pattens like LBP, LTP, an LTP o not pay attention to the spatial istibution popety hien in images. Theefoe, ou geneal iea is to incopoate the spatial istibution popety with the taitional local pattens, so that we can escibe the spatial istibution stuctue of the local textue. This iea leas to the esign of so-calle local spatial istibution patten (LSDP). Given an image I, supposing that p is the efeence pixel in the image I, we use the symbol G V P to enote the gay-level vaiation patten along the hoizontal an vetical iection, an G V P to epesent the patten along the iagonal iection. Then, we etemine G V P an G V P w..t Fomula (1). R D 1, if x y i1 j1 ( ) 2 (, ), ( ) 2 (, ), (, ) G V P i G V P j G V P (1) 0, if x y i1 j1 G V P p f p p G V P p f p p f x y In Fomula (1), R is the numbe of the neighbos aoun p along with the hoizontal an vetical iection, D is the numbe of the neighbos aoun p along 82 Copyight 2014 SERSC

3 with the iagonal iection. Fig.1 shows an example of calculating G V P an G V P. In this example, the exemplifie image egion consists of 3 3 pixels, an both R an D ae p 14 p= Fig. 1. An example of calculating G V P an G V P The efinition of LDSP is base on G V P an G V P. Fist, we use a istance vecto l to enote the istance between the efeence pixel p an its neighbo pixel q in an image, an efine l as l = (q x p x, q y p y ). LSDP of p is efine by integating the hoizontal, vetical o iagonal pattens of these pixels togethe, as efine in Fomula (2). L S D P ( p ) { G V P ( q ), G V P ( p )}, L S D P ( p ) { G V P ( q ), G V P ( p )} (2) Fom Fomula (1) an (2), we integate two 4-bit pattens into 8-bit patten LSDP value fo each efeence pixel. In this pape, we choose the winow size of 3 3 an the istance l as (0, 1), (1, 0), (1, 1), (-1, 1), so we can finally obtain each fou LSDP pattens of the efeence pixel an its neighbo pixel iniviually on hoizontal, vetical an iagonal iections. Afte ientifying the local spatial istibution patten, the whole image is epesente by builing a histogam using Fomula (3), whee H W epesent the size of the input image, x is the LSDP level, ange fom 0 to 255. H W 1, if x y H IS i, j H IS (3) 0, e ls e i 1 j 1 1 H is to g a m ( x ) f ( L S D P ( p ), x ), f ( x, y ) H W Copyight 2014 SERSC 83

4 3 Pefomance Evaluation In oe to evaluate the effectiveness of CLSDP, expeiments ae implemente on a benchmak atabases, name DB1: Coel 10K Database ( DB1 consists of 100 categoies of natual scene images, with 100 images of each categoy, set up a atabase with 10,000 images. This atabase meets all the equiements to evaluate an image etieval system, ue to its lage size an heteogeneous content. Each image in this atabase is consiee as the quey image. The pefomance of LSDP an taitional local pattens is measue in tems of AAP an AAR, as mentione in Fomula (4). D B D B 1 1 A A P ( n ) P e c is io n ( I, n ), A A R ( n ) R e c a ll ( I, n ) i i D B D B (4) i1 i1 The etieval esults ae shown in Fig.2. It shows that the AAP of LSDP inceases fom 39.90% to 42.79% (top matches = 10), an the AAR inceases fom 16.06% to 17.28% (top matches = 100). (a) AAP (b) AAR Fig. 1. AAP an AAR of LSDP an local pattens on DB1 (Coel 10K Database) 4 Conclusion In this pape, we pesent an extension to the local binay pattens fo content-base image etieval. Ou extension is focuse on the spatial popeties of images, an we intouce iectional pattens to bette escibe the textual featues of images. Ou peliminay esults on a eal ata set emonstate its effectiveness. 84 Copyight 2014 SERSC

5 Acknowlegement. This pape is suppote by the National Science Founation of China (No ). Refeences 1. Chen, L., Xu, D., Tsang, I. W., & Luo, J., Tag-base web photo etieval impove by batch moe e-tagging. In Poc. Of CVPR, pp , Li, W., Duan, L., Xu, D., & Tsang, I.-H., Text-base image etieval using pogessive multi-instance leaning. In Poc. Of ICCV, pp , Yu Xia, Shouhong Wan, Peiquan Jin, Lihua Yue, Multi-Scale Local Spatial Binay Pattens fo Content-Base Image Retieval, the 9th Intenational Confeence on Active Meia Technology (AMT 2013), LNCS 8210, 2013, pp Shouhong Wan, Peiquan Jin, Lihua Yue, An Appoach fo Image Retieval Base on Visual Saliency, Intenational Confeence on Image Analysis an Signal Pocessing, 2009 (IASP'09), IEEE CS, Linhai, China, , pp Datta, R., Joshi, D., Li, J., & Wang, J. Z. (2008). Image etieval: Ieas, influences, an tens of the new age. ACM Computing Suveys (CSUR), 40, 5, 2008 Copyight 2014 SERSC 85