Algorithms of Clustering and Classifying Batik Images Based on Color, Contrast and Motif

An interactive system could be provided for batik customers with the aim of helping them in selecting the right batiks. The system should manage a collection of batik images along with other information such as fashion color type, the contrast degree, and motif. This research aims to find methods of clustering and classifying batik images based on fashion color, contrast and motif. A color clustering algorithm using HSV color system is proposed. Two algorithms for contrast clustering, both utilize wavelet, are proposed. Six algorithms for clustering and classifying batik images based on group of motifs, employing shape- and texture-based techniques, are explored and proposed. Wavelet is used in image pre-processing, Canny detector is used to detect image edges. Experiments are conducted to evaluate the performance of the algorithms. The result of experiments shows that fashion color and contrast clustering algorithms perform quite well. Three of motif based clustering and classification algorithms perform fairly well, further work is needed to increase the accuracy and to refine the classification into detailed motif

Integrasi Algoritma Pohon Keputusan C4.5 Yang Dikembangkan Ke Dalam Object-relational Dbms

Integrasi teknik-teknik data mining ke dalam DBMS, khususnya Object-Relational DBMS (ORDBMS), masih merupakan bidang penelitian yang aktif. Isu utama pada integrasi ini adalah: peleburan algoritma data mining ke dalam ORDBMS dengan memanfaatkan fitur-fiturnya untuk memperbaiki kualitas teknik tersebut. Pada penelitian ini, algoritma klasifikasi C4.5 dikembangkan dengan pendekatan aljabar relasional dan diintegrasikan ke dalam ORDBMS sebagai prosedur-prosedur tersimpan Java dan berbasis SQL, dengan tujuan untuk meningkatkan skalabilitas dan efisiensinya. Hasil eksperimen menunjukkan bahwa algoritma yang sudah diintegrasikan berhasil memperbaiki skalabilitas dan pada kasus khusus juga memperbaiki efisiensi

A novel human–machine interface based on recognition of multi-channel facial bioelectric signals

This paper presents a novel human-machine interface for disabled people to interact with assistive systems for a better quality of life. It is based on multi-channel forehead bioelectric signals acquired by placing three pairs of electrodes (physical channels) on the Frontalis and Temporalis facial muscles. The acquired signals are passed through a parallel filter bank to explore three different sub-bands related to facial electromyogram, electrooculogram and electroencephalogram. The root mean square features of the bioelectric signals analyzed within non-overlapping 256 ms windows were extracted. The subtractive fuzzy c-means clustering method (SFCM) was applied to segment the feature space and generate initial fuzzy based Takagi-Sugeno rules. Then, an adaptive neuro-fuzzy inference system is exploited to tune up the premises and consequence parameters of the extracted SFCMs rules. The average classifier discriminating ratio for eight different facial gestures (smiling, frowning, pulling up left/right lips corner, eye movement to left/right/up/down) is between 93.04% and 96.99% according to different combinations and fusions of logical features. Experimental results show that the proposed interface has a high degree of accuracy and robustness for discrimination of 8 fundamental facial gestures. Some potential and further capabilities of our approach in human-machine interfaces are also discussed. © 2011 Australasian College of Physical Scientists and Engineers in Medicine