Barnes, David Preston

Shang, Changjing

English

This paper presents a novel application of support vector machine (SVM) based classifiers for Mars terrain image classification. SVMs are applied in conjunction with information gain ranking (IGR) that allows the induction of informative feature subsets from sample descriptions of feature vectors of a higher dimensionality. Such an integrated use of IGR and SVMs helps to enhance the effectiveness and efficiency of the classifiers, minimizing redundant and noisy features. This work is supported with comparative studies – the resultant SVM-based classifiers generally outperform MLP and KNN-based classifiers and those which use PCA-returned features

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Aberystwyth UniversityCombining Support Vector Machines and Information Gain Ranking forClassification of Mars McMurdo Panorama ImagesBarnes, David Preston; Shang, ChangjingPublication date:2010Citation for published version (APA):Barnes, D. P., & Shang, C. (2010). Combining Support Vector Machines and Information Gain Ranking forClassification of Mars McMurdo Panorama Images. 1061-1064.General rightsCopyright and moral rights for the publications made accessible in the Aberystwyth Research Portal (the Institutional Repository) areretained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by thelegal requirements associated with these rights.            • Users may download and print one copy of any publication from the Aberystwyth Research Portal for the purpose of private study orresearch.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the Aberystwyth Research PortalTake down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.tel: +44 1970 62 2400email: is@aber.ac.ukDownload date: 18. Apr. 2020COMBINING SUPPORT VECTOR MACHINES AND INFORMATION GAIN RANKINGFOR CLASSIFICATION OF MARS MCMURDO PANORAMA IMAGESChangjing Shang and Dave BarnesDept. of Computer Science, Aberystwyth University, UKemail: {cns, dpb}@aber.ac.ukABSTRACTThis paper presents a novel application of support vector ma-chine (SVM) based classiﬁers for Mars terrain image classiﬁcation.SVMs are applied in conjunction with information gain ranking(IGR) that allows the induction of informative feature subsets fromsample descriptions of feature vectors of a higher dimensionality.Such an integrated use of IGR and SVMs helps to enhance the effec-tiveness and efﬁciency of the classiﬁers, minimizing redundant andnoisy features. This work is supported with comparative studies –the resultant SVM-based classiﬁers generally outperform MLP andKNN-based classiﬁers and those which use PCA-returned features.Index Terms— Mars image classiﬁcation, support vector ma-chines, information gain ranking, feature selection.1. INTRODUCTIONAutomated detection and classiﬁcation of objects within Marsimages, including different types of rocks and their surround-ings, is of practical signiﬁcance to the exploration of Martianenvironment [2, 10]. However, Mars images vary consider-ably in terms of intensity, scale and rotation, and are of largescale and blurred with noise [8]. These factors make Marsimage classiﬁcation a very challenging problem, demandingboth effective and efﬁcient techniques.One critical step to successfully classify Mars images isto extract and use informative features only. To capture es-sential image characteristics, many features may have to beextracted without explicit prior knowledge of what propertiesmight best represent the original image. Yet, generating morefeatures increases computational complexity and in the meantime, not all such features may be essential to perform clas-siﬁcation. The use of extra features may even cause the re-duction of the overall discrimination ability of the feature set[4] and hence, that of the classiﬁcation accuracy [5]. Thus, itis desirable to employ feature selection methods that can ﬁndthe most signiﬁcant features, based on sample measurements,to simplify and improve the classiﬁcation process.This paper presents an integrated approach for perform-ing large-scale Mars image classiﬁcation, by exploiting thepotential of advanced classiﬁcation and feature selectiontechniques. In particular, support vector machines (SVMs)[11] are employed for image classiﬁcation. This is due tothe recognition of their high generalization performance incomplex data sets [1]. Information gain-based ranking (IGR)is adopted for feature selection, due to its computation sim-plicity and proven performance [9]. The resulting integratedapproach helps to improve the effectiveness and efﬁciency ofSVM-based image classiﬁers. This is because only those in-formative features are required to be generated in performingactual classiﬁcation, minimizing both the feature measure-ment noise and the computational complexity (of both featureextraction and feature pattern-based classiﬁcation). Sucha property is of great importance to on-board image clas-siﬁcation in future Mars rover missions. This is becauseﬂight projects demand least memory requirement and sim-plest computation possible (in order to minimize loads andincrease software reliability).The paper is organized as follows. Section 2 introducesthe Mars images under investigation. Sections 3, 4 and 5outline the key techniques used, including feature extraction,selection and classiﬁcation. Section 6 shows the experimen-tal results, supported by systematic comparative studies (withMLP and KNN-based classiﬁers that use IGR-selected orPCA-returned features). The paper is concluded in Section 7,where prospects for further research are discussed.2. IMAGE DATABASEThe McMurdo panorama image obtained by NASA’s MarsExploration Rover is shown in Fig. 1, sized 22348 × 5771.This image, excluding the rover’s instrument and shadowedareas, is used for the work here. Sixteen signiﬁcant imagetypes (i.e. classes) are listed in Table 1 and illustrated inFig. 2. The ultimate task of this research is to detect and rec-ognize image regions of such classes.3. FEATURE EXTRACTIONIn this work, local histograms and the ﬁrst and second orderstatistics of color and grey-level images are used to producefeature vectors representing each pixel. Such features are ef-fective in depicting the underlying image characteristics, efﬁ-cient in computation, robust to image translation and rotation[1, 6], thereby suitable for classiﬁcation of Mars images.1061978-1-4244-7993-1/10/$26.00 ©2010 IEEE ICIP 2010Proceedings of 2010 IEEE 17th International Conference on Image Processing September 26-29, 2010, Hong KongFig. 1. Mars McMurdo panorama image with the size of 22348× 5771Fig. 2. Image types (classes)Class Label Class Labeltextured dark rock C1 bedding rock C2mud C3 gray smoothed rock C4black smoothed rock/shadow C5 gravel-1 C6gravel-2 C7 rover tracks-1 C8sand C9 sand ripple C10gravel-3 C11 gravel-4 C12sky C13 rover tracks-2 C14gravel-5 C15 gravel-6 C16Table 1. Image classes and their labels3.1. Color Statistics-Based FeaturesColor images in the RGB (Red, Green and Blue) space areﬁrst transformed to those in the HAS (Hue, Saturation andValue) color space [6]. Features are then generated per pixel,by computing the mean (M ) and the standard deviation (SD)with respect to each of the R, G, B, H, S and V color com-ponents, from a neighborhood of the pixel. The 12 resultingfeatures are denoted by: RM , RSD, GM , GSD, BM , BSD,HM , HSD, SM , SSD, VM , VSD.3.2. Local Histogram-Based FeaturesFor each pixel, a number of color histogram-based featurescan be computed, with respect to each color component, givena ﬁxed bin size and neighborhood [1]. Similarly, another setof local grey-level (GL) histogram features can be generatedby ﬁrst transforming color images to GAL ones. In this work,the bin size for computing color histogram features and thatfor GL histogram features are set to 8 and 16. The resultingcolor histogram-based features are denoted by HHi, SHi, andVHi, i = 1, 2, ..., 8, regarding the H, S, and V components,respectively. The GL features are denoted by GLHj , j =1, 2, ..., 16. Two further GL statistic features, mean and STD,which are denoted by GLM and GLSD, are also generated.4. INFORMATION GAIN-BASED RANKINGLet DX be the value set of feature X and DC be the label setof class variableC. The entropies of the class before and afterobserving X are respectively deﬁned by:H(C) = −∑c∈DC p(c)log2p(c)H(C|X) = −∑x∈DX p(x)∑c∈DC p(c|x)log2p(c|x)The amount by which the entropy of the class decreasesafter observing a certain feature reﬂects the additional infor-mation about the class that feature provides, and is called theinformation gain: IG = H(C) − H(C|X). It measureshow well a given feature separates data points with respect totheir underlying class labels. Thus, all extracted features Xk,k = 1, 2, ..., N , can be ranked with regard to the IG valuesof observing themselves: IGk = H(C) − H(C|Xk). Suchranking can be arranged in descending order, reﬂecting thefact that the higher an IG value is, the more information thecorresponding feature has to offer regarding the class. A sub-set of M most informative features, M ≤ N , can thereforebe selected by choosing the ﬁrst M in the rank list.5. SVM-BASED CLASSIFICATIONSupport vector machines (SVMs) are used to perform im-age classiﬁcation. Such a classiﬁer seeks to ﬁnd the optimalseparating hyperplane among different classes by focusingon those training points (named support vectors), which areplaced at the edge of the underlying feature vectors and whoseremoval would change the solution to be found. Radial Basisfunction (RBF) kernel is adopted in the SVMs. Here, the se-quential minimal optimization algorithm of [7] is used to trainthe SVMs. Detailed SVM learning mechanism is omitted, butcan be found in the literature (e.g. [11]).In order to increase the efﬁcacy of the SVM classiﬁers,IGR is used to rank the extracted features and to select thosemost informative during the training phase. This is of prac-tical signiﬁcance as for on-board application, classiﬁers areexpected to be built with mature technologies (rather than to-tally new mechanisms that have limited experimental data).SVMs are proven high-performance classiﬁers, but they relyon quality input features. Adding SVMs with IGR-based fea-ture selection helps to improve the quality of their input.6. EXPERIMENTAL RESULTSA set of 270 non-overlap images, of a size 512 × 512 each,subdivided from the large image of Fig. 1 (excluding regions1062that represent instruments and shadows) are used in this ex-perimental investigation. In developing each classiﬁer, a col-lection of 2870 pixel points are selected as training data, andanother set of 4070 points as testing data. Each point is la-beled (by expert) with an identiﬁed class index, one of those16 as given in Table 1, and is originally represented by a vec-tor of 54 features (see Section 3). The size of a pixel’s neigh-borhood used for generating feature vectors is set to 15× 15.The SVM penalty parameter is set to 100, with standard Gaus-sian Radial Basis function (RBF) used. In the following com-parative studies, the results of KNNs are ﬁrst obtained withK set to 1, 3, 5, 8, and 10, while for MLPs, only those of onehidden layer are considered, with the number of hidden nodesﬁrst set to 24, 28, 32, or 36. Then, those classiﬁers whichhave the highest accuracy, with respect to a certain number ofK or hidden nodes, are taken to facilitate fair comparison.6.1. Comparison with the Use of Unreduced Feature SetsFor the given training data, the IGR method ranks the original54 features in the following descending order: BM , SSD,GLM , GM , VM , RM , SM , BSD, GSD, GLSD, SH2, SH5,VH2, SH4, SH7, RSD, VSD, SH6, HH7, HSD, GLH4, HH6,GLH2, GLH10, VH3, GLH3, VH1, GLH11, HM , VH5, HH5,GLH5, HH8, GLH9, VH4, GLH8, VH7, GLH12, GLH6,VH6, GLH13, GLH15, HH7, GLH7, GLH14, GLH1, HH4,VH8, SH3, HH6, SH1, GLH16, SH8, HH1. Fig. 3 shows theclassiﬁcation accuracy over the testing set, in relation to howmany top-ranked features (by IGR) are used. The right-mostcase is the result of using all of the 54 original features.5 10 15 20 25 30 35 40 45 50 5550556065707580859095SVM−24F(93.3%)SVM−54F(88.9%MLP−20F(82.3%)MLP−54F(79.3%)KNN29F(80.6%) KNN54F(77.4%)Number of IGR−selected features Correct classification rate (%) SVMMLPKNNSVM−24FSVM−54FMLP−20FMLP−54FKNN−29FKNN−54FFig. 3. Accuracy vs. number of IGR-selected featuresThese results demonstrate that all three types of classiﬁercan have higher classiﬁcation accuracy when IGR-selectedfeatures are used, than using the full set of (54) original fea-tures. This is generally true when the number of IGR-selectedfeatures is greater than 18 for both SVMs and KNNs, and 11for MLPs. In particular, the SVM that uses 24 IGR-selectedfeatures performs the best with a classiﬁcation rate of 93.3%(as marked in Fig. 3). Comparatively, the best MLP and KNNwhich use 20 and 29 top-ranked features respectively, justreach a rate of 82.3% and 80.6%. Also, the classiﬁcation rateusing full features is 88.9% for SVM, 79.3% for MLP, and77.4% for KNN. The employment of IGR not only reducesredundant feature measurements (thereby simplifying classi-ﬁcation process), but also minimizes the noise associated withsuch measurements (thereby improving the classiﬁcation ac-curacy) in SVM, MLP and KNN classiﬁers. The combineduse of SVM and IGR techniques offers the best performance.Based on the above results, the SVMwhich employs those24 IGR-selected features (trained by the use of 2870 featurevectors) is taken to classify the entire Mars McMurdo imageof Fig. 1 (again, excluding equipment and shadows). As an il-lustration, six classiﬁed and segmented image parts are shownin Fig. 5, where 16 different colors represent those 16 im-age types (see Fig 2). From these classiﬁed images, it canbe seen that all image types vary in terms of their size, ro-tation, contrast, shape, and texture. For human eyes it canbe very difﬁcult to identify boundaries between many of suchregions, such as those between types of sand gravel, betweensand and sky, between mud and track sign, and those betweenrock classes. However, the classiﬁer is able to perform undersuch circumstances (with respect to the ground truth painstak-ingly identiﬁed by domain experts).6.2. Comparison with the Use of PCA-returned FeaturesAs principal component analysis (PCA) [3] is arguably oneof the most popular dimensionality reduction methods (al-though it was not initially designed to obtain discriminatoryfeatures), it is adopted here as the benchmark for comparison.Classiﬁers that are aided with IGR are systematically com-pared to those supported by the use of PCA. The results aresummarized in Fig 4. In particular, of the same dimension-ality per type of classiﬁer (i.e. by the use of 24 features forSVMs, 20 for MLPs, and 29 for KNNs), the optimal classi-ﬁers which employ IGR-selected features have a substantiallyhigher classiﬁcation accuracy than those using PCA-returnedfeatures as listed in Table 2. Furthermore, the best performersthat use PCA-returned features only reach a classiﬁcation rateof 87.2%, 81.2% and 79%, whilst requiring the use of 49, 37and 45 features, respectively.5 10 15 20 25 30 35 40 45 50 5565707580859095SVM−24−IGRF(93.3%)MLP−20−IGRF(82.3%)KNN−20−IGRF(80.6%)SVM−PCAF49(87.2%)Number of Principal Component Features Correct Classification Rate (%)SVM−PCAMLP−PCAKNN−PCAFig. 4. IGR-selected vs. PCA-returned features7. CONCLUSIONThis paper has presented a study on Mars terrain image clas-siﬁcation, using SVMs supported by information-gain based1063Method SVM MLP KNNIGR 93.3% 82.3% 80.6%PCA 84.6% 78.8% 77.4%Table 2. Use of IGR-selected vs. PCA-returned featuresfeature selection. For the ﬁrst time, these two techniques areintegrated to help addressing challenging problems in spaceengineering where the real-world images are of many classesand of large-scale. The resultant SVM-based classiﬁers gen-erally outperform MLP and KNN-based classiﬁers and thosewhich use PCA-returned features. This is conﬁrmed by sys-tematic experimental investigations. The employment of IGRnot only simpliﬁes classiﬁcation process, but also improvesthe classiﬁcation accuracy. This work is, therefore, of signiﬁ-cant potential for classiﬁcation and analysis of real images onboard in future Mars rover missions.Interesting further research remains. This includes: com-paring the present work with the use of alternative feature se-lection methods (e.g. fuzzy-rough set-based [10]), determin-ing how the number of optimal IGR-selected features mayvary with respect to different Martian terrain images, and in-corporating IGR into the SVM formulation (instead of usingit as a preprocessing tool for SVM classiﬁers).8. REFERENCES[1] O. Chapelle, P. Haffner, V. N. Vapnik. Support vector machinesfor histogram-based image classiﬁcation. IEEE Tran. on Neu-ral Networks. 10:1055-1064, 1999.[2] H. Dunlop, D. Thompson and D. Wettergreen, Multi-scale fea-tures for detection and segmentation of rocks in Mars images.IEEE Int. Conf. CVPR. 1-7, 2007[3] R. O. Duda, P. E. Hart and D. G. Stork. Pattern Classiﬁcation(2nd edition), Wiley & Sons, New York, 2001.[4] K. Huang and S. Aviyente. Wavelet feature selection for imageclassiﬁcation. IEEE Trans. Image Proc., 17:1709-1720, 2008.[5] R. Jensen, Q. Shen. Are more features better? IEEE Trans.Fuzzy Systems, 17(6):1456-1458, 2009.[6] D. R. Martin, C. C. Fowlkes, J. Malik. Learning to detect natu-ral image boundaries using local brightness, color, and texturecues. IEEE Trans. PAMI. 26:530-549, 2004.[7] J. Platt. Macines using sequential minimal optimisation. in Ad-vances in kernel methods: support vector learning book con-tents. 185 - 208, MIT Press Cambridge, MA, USA. 1999.[8] http://marswatch.astro.cornell.edu/pancam instrument/mcmurdov2.html.[9] D. Iqbal and N. Lee, Efﬁcient feature selection based on infor-mation gain criterion for face recognition, Proc. Int. Conf. onInformation Acquisition. 523-527, 2007.[10] C. Shang, D. Barnes and Q. Shen. Effective feature selectionfor Mars McMurdo image classiﬁcation. Proc. 9th Int. Conf. onIntelligent Systems Design and Applications. 1419-1424, 2009.[11] V. Vapnik. Statistical learning. New Yark, Wiley. 1998.(a) (b)(c) (d)(e) (f)Fig. 5. Classiﬁed and segmented image1064

Combining Support Vector Machines and Information Gain Ranking for Classification of Mars McMurdo Panorama Images

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Combining Support Vector Machines and Information Gain Ranking for Classification of Mars McMurdo Panorama Images

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