Embroidery is the art that is majorly practised in Nigeria, which requires creativity and skills. However, differentiating between two standard embroidery patterns pose challenges to wearers of the patterns. This study developed a classification system to improve the embroiderer to user relationship. The specific characteristics are used as feature sets to classify two common African embroidery patterns (handmade and tinko) are shape, brightness, thickness and colour. The system developed and simulated in MATLAB 2016a environment employed Cellular Learning Automata (CLA) and Support Vector Machine (SVM) as its classifier. The classification performance of the proposed system was evaluated using precision, recall, and accuracy. The system obtained an average precision of 0.93, average recall of 0.81, and average accuracy of 0.97 in classifying the handmade and tinko embroidery patterns considered in this study. This study also presented an experimental result of three validation models for training and testing the dataset used in this study. The model developed an improved and refined embroiderer for eliminating stress related to the manual pattern identification process.

The importance of global features in the analysis of tissue images cannot be overemphasized especially in texture image classification and retrieval. This paper presents different techniques for detection, classification and analysis of diseases pattern in medical images. The research work studies the structure of tissue images; and extracts the similarity features characterized by the Holder exponent for pattern classification. Features from multi-fractal descriptors have been extracted and combined with features from fractal descriptors to generate new descriptors for efficient analysis of images. The experimental procedures have been tested with different extracted features during the classification process to determine the appropriate image features that could yield maximum detection accuracy. The results showed that the descriptors extracted from different features could improve the performance of the models. Our findings in this paper have greatly demonstrated the importance of global features in the analysis of tissue pattern.

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