Farhana Tazmim Pinki

Work place: Computer Science and Engineering Discipline, Khulna University, Khulna, Bangladesh

E-mail: farhana.kucse@gmail.com


Research Interests: Computational Learning Theory, Image Processing, Data Mining


Farhana Tazmim Pinki received her Bachelor of Science in Computer Science and Engineering and Master of Science in Computer Science and Engineering degrees from Science, Engineering and Technology School, Khulna University, Bangladesh. She is currently working as a faculty member in Computer Science and Engineering Discipline, Khulna University. Her research interest includes Machine Learning, Data Mining, and Digital Image Processing.

Author Articles
Colour, Texture, and Shape Features based Object Recognition Using Distance Measures

By S.M. Mohidul Islam Farhana Tazmim Pinki

DOI: https://doi.org/10.5815/ijem.2021.04.05, Pub. Date: 8 Aug. 2021

Object recognition is the recognizing process of objects into semantically expressive classes using its visual insides. Classification of objects becomes complex and challenging task because of its size, poor image quality, occlusion, scaling, geometric distortion, lightening, etc. In this paper, global descriptors that means Color, Texture, and Shape features are used to recognize object. Color histogram is used to obtain the color content, texture content is obtained using Gabor wavelet, and shape content is extracted using Hough transform. These low level or global features are used for creating feature vector. Distance measure is used to find the 1-Nearest Neighbor from the training images i.e. object with minimum distance or maximum similarity with visual contents of the query image. The class of that training image is the predicted label of the query image. We have used twelve different distance measures: some are metrics, some are non-metrics and finally, their recognition accuracy is compared. Ensemble of these distance measures is also used for object recognition in the image. We evaluate this method on a publicly available object-recognition dataset: Columbia Object Image Library (COIL-100) dataset. The experiments show that the recognized results outperform many state-of-the-art methods.

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Variance Analysis Based Mango Recognition Using Correlation Distance

By Farhana Tazmim Pinki S.M. Mohidul Islam

DOI: https://doi.org/10.5815/ijigsp.2020.05.04, Pub. Date: 8 Oct. 2020

Mango plays a major role in the Agro industry and it is a very popular fruit to most of the people due to its flavor and taste. There are many varieties of mangoes that are differentiable based on their various characteristics. Sometimes it is difficult and time consuming for general people or farmers to categorize the mango into different types due to intra-class variation among various types of mangoes. This paper has proposed an automatic system to recognize mangoes thus it becomes convenient to identify various types of mangoes. In this method, mangoes are recognized into different categories based on variance analysis or data dispersion measures. Measures include five number summary, variance, mean deviation, skewness, coefficient of variation which are used as features. From both training and query images, feature vectors are created. Correlation is used to recognize mangoes into various categories. The proposed method shows better result than some existing methods.

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