A study of comparative evaluation of methods for image processing using color features

Transcription

1 A study of comparative evauation of methods for image processing using coor features FLORENTINA MAGDA ENESCU,CAZACU DUMITRU Department Eectronics, Computers and Eectrica Engineering University Pitești Târgu din Vae nr. 1 Pitești jud. Argeș ROMANIA enescu_for@yahoo.com, Abstract: - In this artice sha propose a method for seecting appropriate coor space for purposes of processing images using descriptor in the coor. I chose the coor for that this is the basic characteristic eement of an image. Processing methods proposed, aow seect of the Downoad Descriptor is coor which offers performances higher. Is being tracked in particuar time as we as quaity of retrieving retrieva is successfuy competed. Using in the process of processing, appropriate coor space we wi retrieve images simiar to the one of more powerfu query and more quicky. Key-Words: - coor space, image processing, HSV, RG, 1 3, quantification, precision - reca 1 Introduction The image mining research needs to investigate the foowing probems: a) Propose new representation schemes for visua patterns that are abe to encode sufficient contextua information to aow for meaningfu extraction of usefu visua characteristics; b) Devise efficient content-based image indexing and retrieva techique to faciitate fast and effective access in arge image repository; c) Design semanticay powerfu query anguages for image database [8]; d) Expore new discovery that take into account the unique characteristics of image data; e) Incorporate new techniques for the vizuaization of image of image patterns. eements such as coour, texture and object shape sapatia reationships directy reated to perceptua aspects of image content, together with high-eve concepts the meaning of objects and scenes in the images, are used as cues for retrieving images with simiar content from a database. The variety of knowedge required in visua information retrieva is arge. Different research fieds, which have evoved separatey, provide vauabe contributions to this new research subject. Information retrieva, visua data modeing and computer vision, mutimedia database organization, mutidimensiona indexing, psyhoogica modeing of user behaviour, manmachine interaction and data visuaization, are ony the most important research fieds that contribute in a separate but interreated was to visua information retrieva. 1.1 Visua information retrieva systems Visua information retrieva is a new subject of research in information technoogy. Its purpose is to retrieve, from a database, images or sequences of image that are reevant to a query.it is an extension of traditiona information retrieva designed to incude visua media[1]. Interactivity with visua contents essentia to visua information retrieva. The searching for visua data by reffering directy to its content is the object of new toos and interaction paradigms. Visua 1. From databases to visua information retrieva systems First generation mutimedia database systems focussed on kerne support for bobs (binary arge objects), to efficienty store the sizeabe objects. The second phase concerned techniques for annotation and inking media objects. The database merey contains textua annotations, made accessibe efficienty using conventiona information retrieva techniques. Mutimedia objects remain non-interpreted with respect to retrieva. ISN:

2 The third generation of mutimedia database retrieva research focuses on effective techniques for indexing and retrieva by content [7]. The idea searched for are agorithms to automatiocay index objects according to a semantic framework. In the shorter term, the best we can hope for is to make progress in the effective usage of automaticay derived features that aid pre-seection in a arge mutimedia database. New-generation visua information retrieva systems support fu retrieva by visua content. Access to visua information is not ony performed at a conceptua eve, using keywords as in the textua domain, but aso at a perceptua eve, using objective measurements of the visua content and appropriate simiarity modes. In these systems, image processing, pattern recognition and computer vision are an integra part of the sistem s architecture and operation. They permit the objective anaysis of pixe distibution and the automatic extraction of measurements from raw sensory input. Other sensory data anaysis, ike speech and sound anaysis, can aso be empoyed to extract usefu measurements from video streams. These systems are distinguished according to whether they dea with sti D images, D video or 3D visua data. The rapidy growing WWW environment wi soon require the deveopment of soutions for arge-scae distributed appications. The progres in this fied, demonstrates the feasibity the concept of images retrieva, by using the characteristics based on perceptua images propert of ow-eve such as coour and texture distribution. Taking into account the proprieties required by coor systems to be used in visua search based on content, I have seected for experiment three of them (HSV, RG and 13), for the purpose of determining if they are the best candidates for use in this sense. It is aso presented the system RG coor due to the fact that it is base of other spaces of coor as a resut of the appication of some non-inear transformations or inear. Fig. 1 Processing agorithm and comparison of coor spaces As coor spaces used for image processing Pease, The coor systems must satisfy certain properties imposed by the visua search based on content, namey: 1. The coors system be independent of context;. The coors system be perceptua uniform ; 3. The coors system be inear; 4. The coors system be intuitive; 5. The coors system be robust, i.e.: to be invariante when changing the viewing direction ; to be invariante to changing of objects geometry ; to be invariante to direction change and iumination intensity; to be invariante to changing the spectra energy distribution of ighting..1 HSV coor space When HSV (hue, saturation, vaue) is a noninear transformation of RG coor space. Fig. Transformation RG coor space in HSV.1.1 The transformation of RG coor space in HSV ISN:

3 The reationship between HSV and RG coor space The conversion from RG coor space to HSV coor space is as foows: undefined dacã MAX MIN G 6 +, dacămax R MAX MIN ºi G G H , dacămax R MAX MIN ºi G < R 6 + 1, dacã MAX G MAX MIN R G 6 + 4, dacã MAX MAX MIN, dacã MAX S MIN 1 -, atfe MAX V MAX /55.1. HSV quantification Quantification of HSV space produces a compact set of 166 coors. Fig. 3 Transformation from RG to HSV ecause shade is the most important characteristic of the coor, it requires quantification finest. y quantification are obtained: 18 coors, 3 saturations, 3 vaues and 4 gray coor, ie a tota of 166 distinct coors in HSV coor space.(fig. 3) The pseudocode for quantification procedure: coor h_scae 1 / 18 s_scae 1 / 3 v_scae 1 / 3 If s Then coor 16 + Int(v/(1/4)) If coor 166 Then coor coor 1 Ese If Int(v / v_scae) > 1 Then coorcoor+54*(int(v/v_scae)) If coor Mod 3 * 18 * 3 Then coor coor - 3 * 18 If Int(s / s_scae) > 1 Then coorcoor+18*(int(s/s_scae)) If coor Mod 3 * 18 Then coor coor - 18 If Int(h / h_scae) > 1 Then coorcoor+(int(h/h_scae)) If coor Mod 18 Then coor coor - 1 The 166 coor vaues of the histogram are stored in the database.. RG coor space A inear function of tristimuus vaues converts a set of primary coors in others. Use of the system to query RG images can cause probems when the conditions are different for base image and images of interviewed. Features : - the coor mode : R, G and ; - without transformation; - features: - dependent of the device; -it is not perceptua eveny; - neintuitiv; - dependent on the ange of observation, of the geometry of object, direction, and intensity of the ighting; - remarks: do not require transformation...1 RG quantification The Quantifying of coor space of RG to 64 coors invoves keeping the 4 coors on each axis. The 64 vaues of the coor histogram in the RG space is stored in the database Procedure Cuant_64_coors coor c_scae 64 If red / c_scae > Then coor coor + 16 * Int(red / c_scae) If green / c_scae > Then ISN:

4 coor coor + 4 * Int(green / c_scae) If bue / c_scae > Then coor coor + Int(bue / c_scae).3 Coor space 1 3 When the images RG are correated with each other, it is preferabe to reduce this correation. This can be obtained with the Karhunen-Loeve transformation. This transformation is cacuated on the basis of cover matrix. Three modes have been derived from orthogona coors: 1 R + G + (R - G)/ 3 (G R - )/4 Observe that 1 corresponds to intensity:. Specia features: -the coor mode: L1, L and 3 ; - transformation: 1 3 R + + ( G + - features: - dependent on the device; - it is not perceptua eveny; - inear; - intuitive; - dependent on the changing of coor iumination ; - remarks: mismatch is based on the Karhunen- Love transformation.. Each image in the database has been processed before performing query. This is important because it requires consumption of time and for this reason it is not advisabe to execute at the same time with the query; 3. Has been chosen an image query and have been estabished by a human observer the images considered reevant for that query (Fig. 5) ; 4. Each of images reevant to the query considerated, has been used at a time to interrogate database with the image. Accuracy and the reca represent an average arithmetic mean of vaues resuting in every image taken as an image of querying. 5. To compare the resuts obtained, of each query I did the accuracy graph for each reca. I have presented in tabuar form the vaues that represent the number of images which are reevant in the first five, ten reevant image. Aso number of images to be retrieved that among these to find first five, respective ten reevant image.(fig. 4), (Tabe 1) Fig. 4. The graphic for precision-reca quantification Quantification to 64 vaues sha be in accordance with agorithm aready submitted, resuting other 64 vaues in the space 1 3 who must be stored in the database. 3 Experimenta study The methods are studied from two points of view: - the quaity of retrieva ; - time of execution. For the attainment of this comparative study, for the characteristic of coor I estabished the foowing experimenta conditions: 1. I've created database test; Fig 5 Image used for querying Resuts obtained for each coor space are: Fig. 6 Coor space HSV ISN:

5 Fig. 7 Coor space RG Fig. 8 Coor space 1 3 Statistica resuts regarding the images obtained are presented in the tabe 1. The tabe contains 3 coumns: the first coumn represents coor space used, the second coumn contains the number of reevant image found in the first 5 images and the third coumn contains the number of reevant image found in the first 1 images (number of reevant images retrieved reative to the tota number of items returned). Tabe 1 Statistic on the number of images reevant to the coor spaces RG, HSV. 1 3 A method is more effective, because the curve of its graph is farther than axes of the origin, and, above the other curves. y concuding, we might say that coor space HSV is the better performance. 4 Concusion Has been carried out a comparative anaysis of the methods impemented, for coor images, using descriptor in the coor. We have anayzed a series of methods and coor spaces, in experiment i compared the efficacy of the foowing: - space RG quantify to 64 coors; - space HSV quantify 166 coors; - space 13 quantified to 64 coors. We have chosen these transformations it is necessary that the spaces coor to be: uniform, compete and natura. Athough RG coor space does not meet a of these conditions, I had to choose due to the use of the arge-scae. A study of visua search on the basis of content can be extended in mutipe directions. I coud not determine in which of the three spaces coor anayzed efficiency is greater, by practica experiments with ifting some charts or tabes for the observation performance. The coor is considered to be the most important feature of an image. In studying methods I had in mind: quaity retrieva is successfuy competed, the time of the performance. As you increase the number of reevant image returned by the appication, the more wi increase reca (i.e., more and more reevant images are returned) and decreases accuracy (Whose denominator is even tota number of images returned). A method is a task best as it has the graph is furthest from the origin, or has the vaue of precision greater than to a specific vaue of the parameter redia. References: [1] Guojon Lu. Communication and Computing for Distributed Mutimedia Systems, Artech House 1996 [] N. Koutsoupias, Visuaizing WE navigation patterns with factor anaysis, 7th WSEAS Internationa Conference on Appied Informatics [3] W.Y. Wang, M. C. Lu, C. T. Chuang, J. C. Cheng, Image-ased Height Measuring System, 7th WSEAS Int. Conf. on Signa Processing, Computationa Geometry & Artificia Vision, August 7, pp [4] D. Popescu, R. Dobrescu, M. Nicoae, V. Avram, Agorithm based on medium cooccurrence matrix for image region cassification, 7th WSEAS Int. Conf. on Signa Processing, Computationa Geometry & Artificia Vision, August 7, pp [5] C. Vertan, V. uzuoiu, Tempora Enhancement and Coor ased Motion Detection in Image Sequences, Proc. Of OPTIMU 98, pp ,raşov, Romania, May, [6] R.C. Gonzaes, R. E. Woods, Digita Image Processing, Prentice Ha, Inc, New Jersey, 8. [7] Content-based Mutimedia Data Management and Efficient Remote Access [8] Image Mining: Trends and Deveopments Jizhang, Wynne Hsw, Mong Li Lee/ Nationa University Singapore/1 ISN: