Your SlideShare is downloading. ×
Image texture analysis techniques  survey-1
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Introducing the official SlideShare app

Stunning, full-screen experience for iPhone and Android

Text the download link to your phone

Standard text messaging rates apply

Image texture analysis techniques survey-1

6,500
views

Published on

Published in: Technology

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
6,500
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
7,013
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Image Texture Analysis Techniques- Survey Anita Dixit, Dr.Nagaratna. P Hegde, Asst.Prof, Dept of ISE, SDMCET Associate Prof., Dept of CSE Dharwad. Vasavi College of Engineering, Hyderabad Research Scholar, JNIAS ,Hyderabad nagaratnaph@gmail.com anitadixitjoshi@gmail.comAbstract blocks of pictorial data surrounds the area being This paper discusses the various analyzed. Spectral features describe the averagemethods used to analyze the texture property of tonal variation in various bands of visible and/oran image. Texture analysis is broadly classified electromagnetic spectrum. Whereas Textureinto three categories: Pixel based, local feature feature describe the spatial distribution of tonalbased and Region based. Pixel based method variation with the band. Texture is concerneduses grey level co occurrence matrices,difference histogram and energy measures and with the spatial distribution of grey tones.Local Binary Patterns(LBP) Local feature Texture can be classified into different types,based method uses edges of local features and such as Fine, coarse or smooth, rippled,generalization of co occurrence matrices. irregular or lineated. Texture is innate propertyRegion based method uses region growing and of virtually all surfaces-grain of wood, weave oftopographic models. a fabric, the pattern of crops in afield etc. it Key Words: Co occurrence matrix, contains the important information about theLocal Binary Pattern, Histogram structural arrangement of surfaces and theirMotivation relationship with its surrounding environment. Since textural properties are contain important Texture analysis is one of the use full information in discrimination purpose. Hence itareas of study in machine vision. Human eyes is important to build features for texture. [1]are good judges of differentiating texture of One common approach used tonatural surfaces. Successful vision system is the characterize an images spatial information is toone which realizes this texture to the world extract features for classification which measuresurrounding it[2]. Major goals of texture the spatial arrangement of gray tones within aresearch in computer vision are to understand, neighborhood of a pixel. This feature extractionmodel and process texture, and ultimately to method is referred to as texture analysis andsimulate human visual learning process using includes a multitude of possible features thatcomputer technologies. have been developed to describe image texture.Introduction There are three fundamental featureswith which a human being used to interpret (a) (b) (c)pictorial information; Spectral, Textural andContextual. Spectral information is nothingbut the average tonal variation in various bands.Textual information gives the spatial (d) (e) (f)distribution of tonal variation with in a band. In Fig 1: Variety of textures (a) Tarmac (b) Brick (c)contextual, information is derived from the wood (d) carpet (e) water (f) cloth
  • 2. number of texture analysis techniques and someFig 1 shows the different types of texture which examplesare experienced my human vision system [4] ingeneral. The components of a texture, theTexel(texture element), are notional uniform Statistical method Statistical methods analyze the spatialmicro-objects which are placed in an distribution of gray values, by computing localappropriate way to form any particular texture. features at each point in the image, and derivingIf an intensity variation appears to be perfectly a set of statistics from the distributions of theperiodic, it is called periodic pattern not texture. local features. Depending on the number ofHowever, any completely random pattern would pixels defining the local feature statisticalprobably be called a ‘noise pattern’ rather than a methods can be further classified into first-ordertexture. If a pattern has both regularity and (one pixel), second-order (two pixels) andrandomness then probably it would be called higher-order (three or more pixels) statistics.Texture. The basic difference is that first-order statisticsTexture Analysis : estimate properties (e.g. average and variance) One common approach used to of individual pixel values, ignoring the spatialcharacterize an images spatial information is to interaction between image pixels, whereasextract features for classification which measure second- and higher-order statistics estimatethe spatial arrangement of gray tones within a properties of two or more pixel values occurringneighborhood of a pixel. This feature extraction at specific locations relative to each other. Themethod is referred to as texture analysis[2] and most widely used statistical methods are co occurrence features and gray level differencesincludes a multitude of possible features that [1], which have inspired a variety ofhave been developed to describe image texture. modifications later on. These include signed Texture analysis refers to a class of differences [7] and the LBP (Local Binarymathematical procedures and models that Pattern) operator [6], which incorporatecharacterize the spatial variations within imagery occurrence statistics of simple localas a means of extracting information. Texture is microstructures, thus combining statistical andan areal construct that defines local spatial structural approaches to texture analysis. Otherorganization of spatially varying spectral values statistical approaches include autocorrelationthat is repeated in a region of larger spatial scale. function, which has been used for analyzing the regularity and coarseness of texture and grayThus, the perception of texture is a function of level run lengths but their performance has beenspatial and radiometric scales. Mathematical found to be relatively poorprocedures to characterize texture fall into fourgeneral categories, statistical, geometrical, model- Model Based methodbased methods and signal processing methods and Model-based methods hypothesize theinclude Fourier transforms, convolution filters, co- underlying texture process, constructing aoccurrence matrix, spatial autocorrelation, parametric generative model, which could havefractals, etc. [2] created the observed intensity distribution. The Because texture has so many different intensity function is considered to be adimensions, there is no single method of texture combination of a function representing therepresentation that is adequate for a variety of known structural information on the imagetextures. Here, we provide a brief description of a surface and an additive random noise sequence Geometrical method
  • 3. Geometrical methods consider texture difficult to apply to an image which is to beto be composed of texture primitives, attempting segmented for texture analysisto describe the primitives and the rules Autocorrelation shows the local intensitygoverning their spatial organization. The variation as well as repeatability of the texture.primitives may be extracted by edge detection It is use full for distinguishing short range andwith a Laplacian-of-Gaussian or difference-of- long range order in the texture. Auto correlationGaussian filter Once the primitives have been is not a good discriminator. in natural textures.identified, the analysis is completed either by Hence Co occurrence matrix introduced bycomputing statistics of the primitives (e.g. Harlick et al [1] became a large degree ofintensity, area, elongation, and orientation) or standard.by deciphering the placement rule of the Pixel Based Modelselements The structure and organization of the In pixel based models texture isprimitives can also be presented using Voronoi described by statistics of distribution of greytessellations[2] Image edges are an often used levels or intensities in the texture. Most widelyprimitive element. Harlick et al. [1] generalized used pixel based model is Grey Level Cocooccurrence matrices, which describe second- occurrence model (GLCM). This is firstorder statistics of edges. An alternative to introduced by Harlick et.al [1].generalized cooccurrence matrices is to look forpairs of edge pixels, which fulfill certain Grey Level Occurrence matrix (GLCM)conditions regarding edge magnitude and The fundamental concept behind thesedirection. Properties of the primitives (e.g. area matrices is spatial distribution of grey leveland average intensity) were used as texture elements. In this approach a set of matrices arefeatures created that show the probability that a pair ofSignal Processing Method brightness values (i,j) will occur at a certain Signal processing methods analyze the separation from each other (Δx,Δy). Thefrequency content of the image Spatial domain assumption is that the textural dependence willfilters, such as Law’s masks, local linear be at angles of 0°, 45°, 90° or 135° (with 0°transforms proposed by Unser and Eden (1989), being to the right and 90° above) from theand various masks designed for edge detection are original pixel that means four GLCM matricesthe most direct approach for capturing frequency would have to be created. Consider an image toinformation Rosenfeld and Thurston (1971) be analyzed has Nx resolution horizontally andintroduced the concept of edge density per unit Ny resolution vertically. Grey tone appearing inarea: fine textures tend to have a higher density of each resolution cell is quantized to Ng levels.edges than coarse textures. The set LxXLy is the set of resolution ofTexture Analysis methods: an image ordered in row and column. An image I can be represented as function which assignsAuto correlation and Fourier method. some grey tone in G. we assume that texture- As we know that the texture is property context information in an image I is contained inin which intensity of an image varies region to overall or average spatial relationship which theregion. This prompts us to calculate the variance grey tones in image I have to one another.of intensity over the whole region of a texture. Texture-context information is more adequatelyHowever most of the time this will not provide specified by matrix relative frequencies Pij withenough description which is most of time which two neighboring pixels are separated byneeded. Especially when texels are well defined distance of d occur in an image such matrices ofor where there is high degree of periodicity in grey tone spatial dependency matrices aretexture. Then it is natural to consider the use of function of angular relationship betweenFourier analysis. Moreover Fourier method is
  • 4. neighboring cells as well as the distance Instead of trying to explain texture formation onbetween them. a pixel level, local patterns are formed. EachSince all texture information is present in grey pixel is labeled with the code of the texturetone spatial dependence matrices. Hence all primitive that best matches the localtexture features are extracted from these neighborhood. Thus each LBP code can bematrices. There are total 14 set of features of regarded as a micro-texton. Local primitivesmeasures. But still it is difficult to say which detected by the LBP include spots, flat areas,measure describes which feature of texture. edges, edge ends, curves and so on. SomeFollowing are 3 features out of 14 which define examples are shown in Fig. 3 with the LBP 8,Rthe textural characteristics. They are Angular operator. In the figure, ones are represented asSecond Moment(ASM), Contrast(CON) and white circles, and zeros are black.Correlation(COR)These metrics are calculated for each pixel foreach using each of the four GCLMs and then afinal texture value is usually calculated as an Spot Spot/flat Line end Edge Corneraverage of all four. It is obvious that these Fig 3. Different Texture Primitives detected by LBPmeasurements can be computationallyexpensive especially as the quantization level The LBP distribution has both of the propertiesbecomes large. For many applications it may be of a structural analysis method: texturebeneficial to quantize the image into a smaller primitives and placement rules. On the othernumber of gray levels prior to creating the hand, the distribution is just a statistic of a non-GLCMs linearly filtered image, clearly making the method a statistical one. For these reasons, it isLocal Binary Patterns to be assumed that the LBP distribution can be The local binary pattern (LBP) texture successfully used in recognizing a wide varietyoperator was first introduced as a of texture types, to which statistical andcomplementary measure for local image structural methods have conventionally beencontrast. The first incarnation of the operator applied separately. Ojala et.al [7]worked with the eight-neighbors of a pixel, .using the value of the center pixel as athreshold. An LBP code for a neighborhood wasproduced by multiplying the threshold values Texture Classificationwith weights given to the corresponding pixels, Texture classification refers to assigningand summing up the result sample unknown image to one of a set of known texture classes. Texture classification is one of the four problem domains in the field of texture analysis. The other three are texture segmentation , texture synthesis, shape from texture. Fig4 shows the general fame work used for texture classification.Fig 2.Calculating the original LBP code and a contrastmeasureTopi M¨aenp¨a¨a & Matti Pietik¨ainen[6] haveexplained LBP as follows, The LBP method canbe regarded as a truly unifying approach.
  • 5. unknown samples to be classified is different from that of training data. 3. Rotation invariance; does the algorithm cope, if the rotation of the images changes with respect to the viewpoint.[14] 4. Projection invariance (3-D texture analysis); The algorithm may have to cope with changes in tilt and slant angles. Fig-4. General frame work of texture classification 5. Robustness wrt. noise; how well the algorithm tolerates noise in the inputTexture classification process involves two phases: images.the learning phase and the recognition phase. In the 6. Robustness with respect to parameters;learning phase, the target is to build a model for the the algorithm may have several built-intexture content of each texture class present in the parameters; is it difficult to find the righttraining data, which generally comprises of images values for them, and does a given set ofwith known class labels. The texture content of the values apply for a large range oftraining images is captured with the chosen texture textures.analysis method, which yields a set of textural 7. Computational complexity;features for each image. These features, which can be 8. Generativity; regenerating the texturescalar numbers or discrete histograms or empirical that was captured using the algorithm.distributions, characterize given textural properties of 9. Window/sample size; how large samplethe images, such as spatial structure, contrast, the algorithm requires being able toroughness, orientation, etc. In the recognition phase produce a useful description of thethe texture content of the unknown sample is first texture content.described with the same texture analysis method.Then the textural features of the sample are compared Given a texture description method, theto those of the training images with a classification performance of the method is oftenalgorithm, and the sample is assigned to the category demonstrated using a texture classificationwith the best match. Optionally, if the best match is experiment, which typically comprises ofnot sufficiently good according to some predefined following stepscriteria; the unknown sample can be rejected instead. 1. Selection of image data: 2. Partitioning of the image data into sub Choosing an algorithm for Texture analysis images:. When choosing a texture analysis 3. Preprocessing of the (sub)images:. algorithm, a number of aspects should be 4. Partitioning of the (sub)images data into considered [14] : training and testing sets. 5. Selection of the classification algorithm. 1. Illumination (gray scale) invariance; 6. Definition of the performance criterion: how sensitive the algorithm is to changes two basic alternatives are available, in gray scale. analysis of feature values and class 2. Spatial scale invariance; can the assignments, algorithm cope, if the spatial scale of
  • 6. It is obvious that the final outcome of a ellipses, and so on. Feature extraction tends totexture classification experiment depends on identify the characteristic features that can formnumerous factors, both in terms of the possible a good representation of the object, so as tobuilt-in parameters in the texture description discriminate across the object category withalgorithm and the various choices in the tolerance of variations. [3] .experimental setup. Results of texture Feature Extraction Methodsclassification experiments have always been Serkan Hutipoglu, and Sunjit K.suspect to dependence on individual choices in Mitra[5] suggested two different methods forimage acquisition, preprocessing, sampling etc., texture feature extraction, Quadratic teagersince no performance characterization has been filter and Singular valueestablished in the texture analysis literature decomposition(SVD). Quadratic teager filter is used to find the local energy values. SVDMarkov random field models of texture values are used for feature extraction that represents the low frequency property of an Markov models have long been used for image texture.texture synthesis, to help with the generation ofrealistic images. However, they have alsoproved increasingly useful for texture analysis. ApplicationsIn essence a Markov model is a 1D construct in Four major application domains relatedwhich the intensity at any pixel depends only to texture analysis are texture classification,upon the intensity of the previous pixel in a texture segmentation, shape from texture, andchain and upon a transition probability matrix. texture synthesisTherefore, all experimental results should be For texture analysis normally the image isconsidered to be applicable only to the reported converted to grey scale image. But the use ofsetup. Fortunately, there is some recent work joint color texture method using color histogramaimed at improving the situation with was proposed in [10].standardized test benches, for example the Using color and texture feature is an efficientMeasTex framework for benchmarking texture combination for content based image retrievalclassification algorithms. Additionally, an [17]increasing number of researchers are making the Texture analysis is used majorly remote sensedimagery and algorithms used in their work images. Textural analysis techniques, namelypublicly available in the web fractals and spatial autocorrelation methods, were used to characterize these images in termsFeature Extraction of image complexity and roughness associated Feature extraction (or detection) aims to with forests. The effects of spatial and spectrallocate significant feature regions on images characteristics of the data on the estimates of thedepending on their intrinsic characteristics and textural indices were also examined.applications. These regions can be defined in Fractals are measures of the self-similarity andglobal or local neighborhood and distinguished thus ultimately measure the degree ofby shapes, textures, sizes, intensities, statistical complexity of the imaged land surfaceproperties, and so on. Local feature extraction Spatial autocorrelation is an assessment of themethods are divided into intensity based and correlation of a variable in reference to spatialstructure based. Intensity-based methods location of the variable. Spatial autocorrelationanalyze local intensity patterns to find regions measures the level of interdependence betweenthat satisfy desired uniqueness or stability the variables, the nature and strength of thecriteria. Structure-based methods detect image interdependence.structures such as edges, lines, corners, circles,
  • 7. Conclusion “Texture Feature Extraction Using Teager Texture is one of the important feature Filters And Singular Value Decomposition”,of recognizing an image. It is one such feature IEEE 1998which cannot be defined properly in terms ofcomputer vision. [6] Topi M¨aenp¨a¨a & Matti Pietik¨ainenTypically, a texture starts with a surface that ”Texture Analysis With Local Binary Patterns”,exhibits local roughness or structure which is WSPC,2004then projected to form a textured image. Suchan image exhibits both regularity and [7]. Timo Ojala, MattiPietikaÈinen, “Multirandomness to varying degrees: directionality resolution Gray-Scale and Rotation Invariantand orientation will also be relevant parameters Texture Classification with Local Binaryin a good many cases. However, the essential Patterns”, IEEE Transactions on Patternfeature of randomness means that textures have Analysis and Machine intelligence, vol. 24, no.to be characterized by statistical techniques, and 7, July 2002recognized using statistical classification [8]. M. PIETIKÄINEN, T. OJALA and Z. XU,procedures. Techniques that have been used for “Rotation-Invariant Texture Classificationthis purpose have been seen to include Using Feature Distributions”, M.autocorrelation, co-occurrence matrices, texture PIETIKÄINEN, T. OJALA and Z. XU, Patternenergy measures, fractal-based measures, recognition, Vol 33,No1,pp43-52,2000Markov random fields, and so on. [9]. Zhi Li, Guizhong Liu, Yang Yang, and In this paper I have made an effort to Junyong You, “Scale- and Rotation-Invariantpresent the various methods of texture analysis, Local Binary Pattern Using Scale-Adaptivetexture classification and their applications. Texton and Sub uniform-based Circular Shift References [10]. Matti Pietik.inen, Topi M.enp.. and Jaakko[1]. Harlick and Shanmugam, “Textural feature Viertola” Color Texture Classification withfor image classification”, IEEE 1973 Color Histograms and Local Binary Patterns”,[2] Mihran Tuceryan, Anil K. Jain, “Texture [11].Zhenhua Guo, Lei Zhang, A CompletedAnalysis”, The Handbook of Pattern Modeling of Local Binary Pattern Operator for Texture Classification, submitted to IEEERecognition and Computer Vision (2nd transaction 2010Edition), by C. H. Chen, L. F. Pau, P. S. P.Wang (eds.), pp. 207-248, World Scientific [12] Mehrdad J. Gangeh,_, Robert P.W. Duin,Publishing Co., 1998. Bart M. ter Haar Romeny, Mohamed S. Kamel, A Two-Stage Combined Classifier in Scale[3] FRANK Y. SHIH, “Image processing and Space Texture, Classification Elsevier, Julypattern Recognition Fundamentals and 2012.Techniques”, Image Processing and PatternRecognition, IEEE 2010. [13] K. Muneeswaran, L. Ganesan, S. Arumugam, K. Ruba Soundar, Texture[4] E. R. Davies, “Introduction to Texture classification with combined rotation and scaleAnalysis”, HANDBOOK OF TEXTURE invariant wavelet features, Pattern RecognitionANALYSIS ,Imperial College Press 38 (2005) pp 1495-1506.[5] Serkan Hutipoglu, and Sunjit K. Mitra,
  • 8. [14] T Ojala and M Pietikäinen, Textureclassification,1998 [18] Manik Varma and Andrew Zisserman, A Statistical Approach to Texture Classification[15] L.Li, C.S Tong, S.K Choy, Texture from Single Images, Kluwer AcademicClassification using refined histogram. IEEE Publishers,2004Transaction vol 19,no 5,May 2010 [19]. C. Laymon and M. Al-Hamdan, “Information Extraction from Remote Sensing[16] Minh N. Do and Martin Vetterli, Rotation Imagery Using Textural Analysis “,NationalInvariant Texture Characterization and Retrieval Consortium on Remote Sensing inusing Steerable Wavelet-domain HiddenMarkov Transportation, Dec2005Models [20]. Timo Ojala, Matti Pietika¬inen,[17] Young Deok Chun, Nam Chul Kim, “Unsupervised texture segmentation usingContent-Based Image Retrieval Using Multi feature distributions”, Pattern Recognition 32resolution Color and Texture Features, IEEE (1999) 477-486Transactions On Multimedia, Vol. 10, No. 6,October 2008