Two-stage binary classifier for neuromuscular disorders using surface electromyography feature extraction and selection

Obyek dinyatakan bergerak jika terjadi perubahan posisi dimensi disetiap frame. Pergerakan obyek menyebabkan obyek memiliki perbedaan bentuk pola disetiap frame-nya. Frame yang memiliki pola terbaik diantara frame lainnya disebut frame dominan. Penelitian ini bertujuan untuk menyeleksi frame dominan dari rangkaian frame dengan menerapkan metode K-means clustering untuk memperoleh centroid dominan (centroid dengan nilai tertinggi) yang digunakan sebagai dasar seleksi frame dominan. Dalam menyeleksi frame dominan terdapat 4 tahapan utama yaitu akuisisi data, penetapan pola obyek, ekstrasi ciri dan seleksi. Data yang digunakan berupa data video yang kemudian dilakukan proses penetapan pola obyek menggunakan operasi pengolahan citra digital, dengan hasil proses berupa pola obyek RGB yang kemudian dilakukan ekstraksi ciri berbasis NTSC dengan menggunakan metode statistik orde pertama yaitu Mean. Data hasil ekstraksi ciri berjumlah 93 data frame yang selanjutnya dikelompokkan menjadi 3 cluster menggunakan metode K-Means. Dari hasil clustering, centroid dominan terletak pada cluster 3 dengan nilai centroid 0.0177 dan terdiri dari 41 data frame. Selanjutnya diukur jarak kedekatan seluruh data cluster 3 terhadap centroid, data yang memiliki jarak terdekat dengan centroid itulah frame dominan. Hasil seleksi frame dominan ditunjukkan pada jarak antar centroid dengan anggota cluster, dimana dari seluruh 41 data frame tiga jarak terbaik diperoleh adalah 0.0008 dan dua jarak bernilai 0.0010 yang dimiliki oleh frame ke-59, ke-36 dan ke-35. AbstractThe object is declared moving if there is a change in the position of the dimensions in each frame. The movement of an object causes the object to have different shapes in each frame. The frame that has the best pattern among other frames is called the dominant frame. This study aims to select the dominant frame from the frame set by applying the K-means clustering method to obtain the dominant centroid (the highest value centroid) which is used as the basis for the selection of dominant frames. In selecting dominant frames, there are 4 main stages, namely data acquisition, determination of object patterns, feature extraction and selection. The data used in the form of video data which is then carried out the process of determining the pattern of objects using digital image processing operations, with the results of the process in the form of an RGB object pattern which is then performed NTSC-based feature extraction using the first-order statistical method, Mean. The data from feature extraction are 93 data frames which are then grouped into 3 clusters using the K-Means method. From the results of clustering, the dominant centroid is located in cluster 3 with a centroid value of 0.0177 and consists of 41 data frames. Furthermore, the proximity of all data cluster 3 to the centroid is measured, the data having the closest distance to the centroid is the dominant frame. The results of dominant frame selection are shown in the distance between centroids and cluster members, where from all 41 data frames the three best distances obtained are 0.0008, 0.0010, and 0.0010 owned by 59th, 36th and 35th frames.

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VESSEL CLASSIFICATION IN COSMO-SKYMED SAR DATA USING HIERARCHICAL FEATURE SELECTION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w3-975-2015 ◽

2015 ◽

Vol XL-7/W3 ◽

pp. 975-982 ◽

Cited By ~ 5

Author(s):

A. Makedonas ◽

C. Theoharatos ◽

V. Tsagaris ◽

V. Anastasopoulos ◽

S. Costicoglou

Keyword(s):

Feature Extraction ◽

Feature Selection ◽

Selection Procedure ◽

Texture Features ◽

Statistical Characteristics ◽

Selection Scheme ◽

Feature Extraction And Selection ◽

Sar Data ◽

Ship Classification ◽

Analysis Scale

SAR based ship detection and classification are important elements of maritime monitoring applications. Recently, high-resolution SAR data have opened new possibilities to researchers for achieving improved classification results. In this work, a hierarchical vessel classification procedure is presented based on a robust feature extraction and selection scheme that utilizes scale, shape and texture features in a hierarchical way. Initially, different types of feature extraction algorithms are implemented in order to form the utilized feature pool, able to represent the structure, material, orientation and other vessel type characteristics. A two-stage hierarchical feature selection algorithm is utilized next in order to be able to discriminate effectively civilian vessels into three distinct types, in COSMO-SkyMed SAR images: cargos, small ships and tankers. In our analysis, scale and shape features are utilized in order to discriminate smaller types of vessels present in the available SAR data, or shape specific vessels. Then, the most informative texture and intensity features are incorporated in order to be able to better distinguish the civilian types with high accuracy. A feature selection procedure that utilizes heuristic measures based on features’ statistical characteristics, followed by an exhaustive research with feature sets formed by the most qualified features is carried out, in order to discriminate the most appropriate combination of features for the final classification. In our analysis, five COSMO-SkyMed SAR data with 2.2m x 2.2m resolution were used to analyse the detailed characteristics of these types of ships. A total of 111 ships with available AIS data were used in the classification process. The experimental results show that this method has good performance in ship classification, with an overall accuracy reaching 83%. Further investigation of additional features and proper feature selection is currently in progress.

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