A robust adaptive wavelet-based method for classification of meningioma histology images

Intra-class variability in the texture of samples is an important problem in the domain of histological image classification. This issue is inherent to the field due to the high complexity of histology image data. A technique that provides good results in one trial may fail in another when the test and training data are changed and therefore, the technique needs to be adapted for intra-class texture variation. In this paper, we present a novel wavelet based multiresolution analysis approach to meningioma subtype classification in response to the challenge of data variation.We analyze the stability of Adaptive Discriminant Wavelet Packet Transform (ADWPT) and present a solution to the issue of variation in the ADWPT decomposition when texture in data changes. A feature selection approach is proposed that provides high classification accuracy

Feature selection using Haar wavelet power spectrum

BACKGROUND: Feature selection is an approach to overcome the 'curse of dimensionality' in complex researches like disease classification using microarrays. Statistical methods are utilized more in this domain. Most of them do not fit for a wide range of datasets. The transform oriented signal processing domains are not probed much when other fields like image and video processing utilize them well. Wavelets, one of such techniques, have the potential to be utilized in feature selection method. The aim of this paper is to assess the capability of Haar wavelet power spectrum in the problem of clustering and gene selection based on expression data in the context of disease classification and to propose a method based on Haar wavelet power spectrum. RESULTS: Haar wavelet power spectra of genes were analysed and it was observed to be different in different diagnostic categories. This difference in trend and magnitude of the spectrum may be utilized in gene selection. Most of the genes selected by earlier complex methods were selected by the very simple present method. Each earlier works proved only few genes are quite enough to approach the classification problem [1]. Hence the present method may be tried in conjunction with other classification methods. The technique was applied without removing the noise in data to validate the robustness of the method against the noise or outliers in the data. No special softwares or complex implementation is needed. The qualities of the genes selected by the present method were analysed through their gene expression data. Most of them were observed to be related to solve the classification issue since they were dominant in the diagnostic category of the dataset for which they were selected as features. CONCLUSION: In the present paper, the problem of feature selection of microarray gene expression data was considered. We analyzed the wavelet power spectrum of genes and proposed a clustering and feature selection method useful for classification based on Haar wavelet power spectrum. Application of this technique in this area is novel, simple, and faster than other methods, fit for a wide range of data types. The results are encouraging and throw light into the possibility of using this technique for problem domains like disease classification, gene network identification and personalized drug design

A Comparative Study of Two State-of-the-Art Feature Selection Algorithms for Texture-Based Pixel-Labeling Task of Ancient Documents

International audienceRecently, texture features have been widely used for historical document image analysis. However, few studies have focused exclusively on feature selection algorithms for historical document image analysis. Indeed, an important need has emerged to use a feature selection algorithm in data mining and machine learning tasks, since it helps to reduce the data dimensionality and to increase the algorithm performance such as a pixel classification algorithm. Therefore, in this paper we propose a comparative study of two conventional feature selection algorithms, genetic algorithm and ReliefF algorithm, using a classical pixel-labeling scheme based on analyzing and selecting texture features. The two assessed feature selection algorithms in this study have been applied on a training set of the HBR dataset in order to deduce the most selected texture features of each analyzed texture-based feature set. The evaluated feature sets in this study consist of numerous state-of-the-art texture features (Tamura, local binary patterns, gray-level run-length matrix, auto-correlation function, gray-level co-occurrence matrix, Gabor filters, Three-level Haar wavelet transform, three-level wavelet transform using 3-tap Daubechies filter and three-level wavelet transform using 4-tap Daubechies filter). In our experiments, a public corpus of historical document images provided in the context of the historical book recognition contest (HBR2013 dataset: PRImA, Salford, UK) has been used. Qualitative and numerical experiments are given in this study in order to provide a set of comprehensive guidelines on the strengths and the weaknesses of each assessed feature selection algorithm according to the used texture feature set

Pengenalan Ekspresi Wajah Menggunakan Kombinasi Gabor Wavelet, Adaptive Boosting Feature Selection, dan Support Vector Machine

ABSTRAKSI: Pengenalan ekspresi wajah dari suatu citra merupakan salah satu bidang yang masih sangat menarik perhatian banyak peneliti dalam beberapa dekade belakangan ini. Pada pengenalan ekspresi wajah, ada dua tahap yang harus dilakukan, yaitu pengekstraksian ciri dari suatu citra inputan dan pengklasifikasian citra tersebut ke dalam kelas ekspresi tertentu. Tugas akhir ini mencoba menyisipkan tahapan lain yaitu seleksi fitur dengan menggunakan metode Adaptive Boosting Feature Selection (AdaFs), ke dalam sistem pengenalan ekspresi wajah menggunakan metode Gabor Wavelet, pada tahap ektraksi fitur, dan metode Support Vector Machine (SVM), pada tahap klasifikasinya.Seleksi fitur oleh AdaFs memilih fitur-fitur tertentu, dari kumpulan fitur Gabor, yang dianggap paling mendiskriminasi data dari kelas ekspresi tertentu dengan data dari kelas ekspresi lainnya, yang tidak menyebabkan performansi klasifikasinya menjadi menurun. Pengujian dilakukan dengan mengimplementasikan metode k-folds cross validation, dan membagi data latih dan data uji ke dalam 3 partisi. Hasil uji coba terhadap semua kombinasi partisi data menunjukkan bahwa proses seleksi fitur dapat mempengaruhi tingkat akurasi pengenalan sistem, baik meningkatkan maupun justru menurunkan performansinya, tergantung dari ketepatan pemilihan jumlah fiturnya. Selain itu, pengadaptasian metode voting SVM multiclass One-Against-All (OAA) dan One-Against-One (OAO) juga mempengaruhi tingkat akurasi sistem. Dari hasil pengujian, akurasi tertinggi didapatkan ketika pengujian dilakukan dengan data set ke-3 dengan klasifier OAA dengan fitur yang dipilih berjumlah 371 fitur, dimana tingkat akurasinya mencapai 95%.Kata Kunci : Pengenalan Ekspresi Wajah, Gabor Wavelet, Seleksi Fitur, Adaptive Boosting Feature Selection, Support Vector Machine, One-Against-All, One-Against-OneABSTRACT: In last decades, Facial Expression Recognition from an image still becomes one of many researchers\u27 interests. In Facial Expression Recognition, there are two main steps that play important role for the purpose of recognition, those are feature extraction, that determines image representation, and classification which classify that image into specific expression class. This TA project tries to add another step by using features selection, with Adaptive Boosting Feature Selection as its method, into facial expression recognition system which is built using Gabor Wavelet, as feature extraction method, and Support Vector Machine, as its classification method.AdaFs selects significant features, from the pool of Gabor features, considered as unique ones, that can discriminate data of one class from other classes, without decreasing performace of the classifier. Experiments are done by implementing the k-folds cross validation in testing stage and dividing data into 3 partitions. The results of all combinations of data partitions indicate that the feature selection process can affect the accuracy of recognition systems, either increase or actually decrease its performance, depending on the exactness of selecting number of features. In addition, the using of voting methods of multiclass SVM, which are One-Against-All (OAA) and One-Against-One (OAO), also affects the accuracy of the system. From the experiments, the highest accuracy, which reached 95% accuracy level, is obtained when testing is done with the use of the third data set and the use of OAA classifier with the number of selected features is 371.Keyword: Facial Expression Recognition, Gabor Wavelet, Features Selection, Adaptive Boosting Feature Selection, Support Vector Machine, One-Against-All, One-Against-On

Hyperspectral colon tissue cell classification

A novel algorithm to discriminate between normal and malignant tissue cells of the human colon is presented. The microscopic level images of human colon tissue cells were acquired using hyperspectral imaging technology at contiguous wavelength intervals of visible light. While hyperspectral imagery data provides a wealth of information, its large size normally means high computational processing complexity. Several methods exist to avoid the so-called curse of dimensionality and hence reduce the computational complexity. In this study, we experimented with Principal Component Analysis (PCA) and two modifications of Independent Component Analysis (ICA). In the first stage of the algorithm, the extracted components are used to separate four constituent parts of the colon tissue: nuclei, cytoplasm, lamina propria, and lumen. The segmentation is performed in an unsupervised fashion using the nearest centroid clustering algorithm. The segmented image is further used, in the second stage of the classification algorithm, to exploit the spatial relationship between the labeled constituent parts. Experimental results using supervised Support Vector Machines (SVM) classification based on multiscale morphological features reveal the discrimination between normal and malignant tissue cells with a reasonable degree of accuracy

Multiscale Discriminant Saliency for Visual Attention

The bottom-up saliency, an early stage of humans' visual attention, can be considered as a binary classification problem between center and surround classes. Discriminant power of features for the classification is measured as mutual information between features and two classes distribution. The estimated discrepancy of two feature classes very much depends on considered scale levels; then, multi-scale structure and discriminant power are integrated by employing discrete wavelet features and Hidden markov tree (HMT). With wavelet coefficients and Hidden Markov Tree parameters, quad-tree like label structures are constructed and utilized in maximum a posterior probability (MAP) of hidden class variables at corresponding dyadic sub-squares. Then, saliency value for each dyadic square at each scale level is computed with discriminant power principle and the MAP. Finally, across multiple scales is integrated the final saliency map by an information maximization rule. Both standard quantitative tools such as NSS, LCC, AUC and qualitative assessments are used for evaluating the proposed multiscale discriminant saliency method (MDIS) against the well-know information-based saliency method AIM on its Bruce Database wity eye-tracking data. Simulation results are presented and analyzed to verify the validity of MDIS as well as point out its disadvantages for further research direction.Comment: 16 pages, ICCSA 2013 - BIOCA sessio