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KERNEL METHODS IN IMAGE PROCESSING

Dans le document Encyclopedia of Data Warehousing and Mining (Page 132-137)

Applications of Kernel Methods

KERNEL METHODS IN IMAGE PROCESSING

Applications of Kernal Methods

regression algorithm for prediction and no analytical tools exist. In this case, one can actually use an SVM to solve the part of the problem where no analytical solution exist and combine the solution with other existing analytical and closed form solutions.

The use of kernelized SVMs has been already proposed to solve a variety of digital communications problems. The decision feedback equalizer (Sebald &

Buclew, 2000) and the adaptive multi-user detector for Code Division Multiple Access (CDMA) signals in multipath channels (Chen et al., 2001)are addressed by means of binary SVM nonlinear classifiers. In (Rah-man et al., 2004) signal equalization and detection for a MultiCarrier (MC)-CDMA system is based on an SVM linear classification algorithm. Koutsogiannis et al. (2002) introduced the use of KPCA for classification and de-noising of communication signals.

KERNEL METHODS IN SIGNAL PROCESSING

Many signal processing supervised and unsupervised schemes such as discriminant analysis, clustering, principal/independent component analysis, or mutual information extraction have been addressed using kernels (see previous chapters). Also, an interesting perspective for signal processing using SVM can be found in (Mattera, 2005), which relies on a different point of view to signal processing.

The use of time series with supervised SVM al-gorithms has mainly focused on two DSP problems:

(1) non-linear system identification of the underlying relationship between two simultaneously recorded discrete-time processes, and (2) time series predic-tion (Drezet and Harrison 1998; Gretton et al., 2001;

Suykens, 2001). In both of them, the conventional SVR considers lagged and buffered samples of the available signals as its input vectors.

These approaches pose several problems and op-portunities. First, the statement of linear signal models in the primal problem, which will be called SVM primal signal models, will allow us to obtain robust estimators of the model coefficients (Rojo-Álvarez et al., 2005a) in classical DSP problems, such as ARMA modeling, the g-filter, and spectral analysis (Rojo-Álvarez et al., 2003, Camps-Valls et al., 2004, Rojo-Álvarez et al., 2004). Second, the consideration of nonlinear SVM-DSP algorithms can be addressed from two different

approaches: (1) RKHS signal models, which state the signal model equation in the feature space (Martínez-Ramón et al., 2005), and (2) dual signal models, which are based on the nonlinear regression of each single time instant with appropriate Mercer’s kernels (Rojo-Álvarez et al., 2005b).

KERNEL METHODS IN SPEECH PROCESSING

An interesting and active research field is that of speech recognition and speaker verification. First, there have been many attempts to apply SVMs to improve exist-ing speech recognition systems. Ganapathiraju (2002) uses SVMs to estimate Hidden Markov Models state likelihoods, Venkataramani et al. (2003) applied SVMs to refine the decoding search space, and in (Gales and Layton, 2004) statistical models for large vocabulary continuous speech recognition were trained using SVMs. Second, early SVM approaches by Schmidt and Gish (1996), and then by Wan and Campbell (2000), used polynomial and RBF kernels to model the distri-bution of cepstral input vectors. Further improvements considered mapping to feature space using sequence kernels (Fine et al. 2001). In the case of speaker veri-fication, the recent works of Shriberg et al. (2005) for processing high-level stylistic or lexical features are worth mentioning.

Voice processing has been performed by using KPCA. Lima et al. (2005) used sparse KPCA for voice feature extraction and then used them for speech rec-ognition. Mak et al. (2005) used KPCA to introduce speaker adaptation in voice recognition schemes.

KERNEL METHODS IN IMAGE PROCESSING

One of the first works proposing kernel methods in the context of image processing was (Osuna et al., 1997), where a face detection system was proposed. Also, in (Papagiorgiou & Poggio, 2000) a face, pedestrian, and car detection method based on SVMs and Haar wavelets to represent images was presented.

The previous global approaches demonstrated good results for detecting objects under fixed viewing condi-tions. However, problems occur when the viewpoint and pose vary. Different methods have been built to

Applications of Kernal Methods

tackle these problems. For instance, the component-

A

based approach (Heisele et al., 2001) alleviates this face detection problem. Nevertheless, the main issues in this context are related to: the inclusion of geometric relationships between components, which were partially addressed in (Mohan et al., 2001) using a two-level strategy; and automatically choose components, which was improved in (Heisele et al., 2002) based on the incorporation of 3D synthetic face models database.

Alternative approaches, completely automatic, have been later proposed in the literature (Ullman et al. 2002), and kernel direct discriminate analysis (KDDA) was used by Lu et al (2006) for face recognition.

Liu et al. (2004) used KICA to model face appear-ance, showing that the method is robust with respect to illumination, expression and pose variations. Zheng et al. (2006) used KKCA for facial expression recogni-tion. Another application is object recognirecogni-tion. In the special case where the objects are human faces, it opens to face recognition, an extremely lively research field, with applications to video surveillance and security (see http://www.face-rec.org/). For instance, (Pontil

& Verri, 1998) identified objects in the COIL database (http://www1.cs.columbia.edu/CAVE/). Vaswani et al.

(2006) use KPCA for image and video classification.

Texture classification using kernel independent compo-nent analysis has been, for example, used by Cheng et al. (2004), and KPCA, KCCA and SVM are compared in Horikawa (2005). Finally, it is worth mentioning the matching kernel (Wallraven et al., 2003), which uses local image descriptors; a modified local kernel (Boughorbel, 2005), or the pyramid local descriptions (Grauman & Darrell, 2005).

Kernel methods have been used in multi-dimensional images, i.e. those acquired in (relatively high) number N of spectral bands acquired from airborne or satel-lite sensors. Support Vector Machines (SVMs) were first applied to hyperspectral image classification in (Gualtieri & Cromp, 1998) and their capabilities were further analyzed in (Camps-Valls et al., 2004) in terms of stability, robustness to noise, and accuracy. Some other kernel methods have been recently presented to improve classification, such as the kernel Fisher dis-criminant (KFD) analysis (Dundar & Langrebe, 2004), or Support Vector Clustering (SVC) (Song, Cherian, &

Fan, 2005). In (Camps-Valls & Bruzzone, 2005), an extensive comparison of kernel-based classifiers was conducted in terms of the accuracy of methods when

working in noisy environments, high input dimension, and limited training sets. Finally, a full family of com-posite kernels for efficient combination of spatial and spectral information in the scene has been presented in (Camps-Valls, 2006).

Classification of functional magnetic resonance images (fMRI) is a novel technique that may lead to a quantity of discovery tools in neuroscience. Clas-sification in this domain is intended to automatically identify differences in distributed neural substrates resulting from cognitive tasks. The application of kernel methods has given reasonable results in accuracy and generalization ability. Recent work by Cox and Savoy (Cox and Savoy, 2003) demonstrated that linear dis-criminant analysis (LDA) and support vector machines (SVM) allow discrimination of 10 class visual activation patterns evoked by the visual presentation of various categories of objects on a trial-by-trial basis within individual subjects. LaConte et al., (2005) used a linear SVM for online pattern recognition of left and right motor activation in single subjects. Wang et al (Wang et al., 2004) applied an SVM classifier to detect brain cognitive states across multiple subjects. In (Martínez-Ramón et al., 2005a), a work has been presented that splits the activation maps into areas, applying a local (or base) classifier to each one. In (Koltchinskii et al, 2005), theoretical bounds on the performance of the method for the binary case have been presented, and in (Martínez-Ramón et al, 2006), a distributed boosting takes advantage of the fact that the distribution of the information in the brain is sparse.

FUTURE TRENDS

Kernel methods have been applied in bioinformatics, signal and speech processing, and communications, but there are many areas of science and engineering in which these techniques have not been applied, namely the emerging techniques of chemical sensing (such as olfaction), forecasting, remote sensing, and many others. Our prediction is that, provided that kernel methods are systematically showing improved results over other techniques, these methods will be applied in a growing amount of engineering areas, as long as to an increasing amount of activity in the areas surveyed in this chapter.

Applications of Kernal Methods

CONCLUSION

This chapter has revised the main applications encoun-tered in the active field of machine learning known as kernel methods. This well-established field has emerged very useful in many application domains, mainly due to the versatility of the provided solutions, the possibility to adapt the method to the application field, the math-ematical elegance and many practical properties. The interested reader can find more information on these application domains in (Camps-Valls, 2006), where a suite of applications and novel kernel developments are provided. The application and development of kernel methods to new fields and also the challenging questions answered so far ensure exciting results in the near future.

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Applications of Kernal Methods

Dans le document Encyclopedia of Data Warehousing and Mining (Page 132-137)