Showing papers by "Hossam M. Zawbaa published in 2014"

Automatic fruit classification using random forest algorithm

Abstract: The aim of this paper is to develop an effective classification approach based on Random Forest (RF) algorithm. Three fruits; i.e., apples, Strawberry, and oranges were analysed and several features were extracted based on the fruits' shape, colour characteristics as well as Scale Invariant Feature Transform (SIFT). A preprocessing stages using image processing to prepare the fruit images dataset to reduce their color index is presented. The fruit image features is then extracted. Finally, the fruit classification process is adopted using random forests (RF), which is a recently developed machine learning algorithm. A regular digital camera was used to acquire the images, and all manipulations were performed in a MATLAB environment. Experiments were tested and evaluated using a series of experiments with 178 fruit images. It shows that Random Forest (RF) based algorithm provides better accuracy compared to the other well know machine learning techniques such as K-Nearest Neighborhood (K-NN) and Support Vector Machine (SVM) algorithms. Moreover, the system is capable of automatically recognize the fruit name with a high degree of accuracy.

Automatic Fruit Image Recognition System Based on Shape and Color Features

Abstract: This paper presents an automatic fruit recognition system for classifying and identifying fruit types. The work exploits the fruit shape and color, to identify each image feature. The proposed system includes three phases namely: pre-processing, feature extraction, and classification phases. In the pre-processing phase, fruit images are resized to 90 x 90 pixels in order to reduce their color index. In feature extraction phase, the proposed system uses scale invariant feature transform (SIFT) and shape and color features to generate a feature vector for each image in the dataset. For classification phase, the proposed model applies K-Nearest Neighborhood (K-NN) algorithm classification, and support vector machine (SVM) algorithm of different kinds of fruits. A series of experiments were carried out using the proposed model on a dataset of 178 fruit images. The results of carrying out these experiments demonstrate that the proposed approach is capable of automatically recognize the fruit name with a high degree of accuracy.

Retinal vessel segmentation based on possibilistic fuzzy c-means clustering optimised with cuckoo search

Abstract: Automated analysis of retinal vessels is essential for the diagnosis of a wide range of eye diseases and plays an important role in automatic retinal disease screening systems. In this paper, we present an approach to automatic vessel segmentation in retinal images that utilises possibilistic fuzzy c-means (PFCM) clustering to overcome the problems of the conventional fuzzy c-means objective function. In order to obtain optimised clustering results using PFCM, a cuckoo search method is used. The cuckoo search algorithm, which is based on the brood parasitic behaviour of some cuckoo species in combination with the Levy flight behaviour of some birds and fruit flies, is applied to drive the optimisation of the fuzzy clustering. The performance of our algorithm is analysed on two benchmark databases, the DRIVE and STARE datasets, and encouraging segmentation performance is observed.

Retinal blood vessel segmentation using bee colony optimisation and pattern search

Abstract: Accurate segmentation of retinal blood vessels is an important task in computer aided diagnosis of retinopathy. In this paper, we propose an automated retinal blood vessel segmentation approach based on artificial bee colony optimisation in conjunction with fuzzy c-means clustering. Artificial bee colony optimisation is applied as a global search method to find cluster centers of the fuzzy c-means objective function. Vessels with small diameters appear distorted and hence cannot be correctly segmented at the first segmentation level due to confusion with nearby pixels. We employ a pattern search approach to optimisation in order to localise small vessels with a different fitness function. The proposed algorithm is tested on the publicly available DRIVE and STARE retinal image databases and confirmed to deliver performance that is comparable with state-of-the-art techniques in terms of accuracy, sensitivity and specificity.

Networks Community Detection Using Artificial Bee Colony Swarm Optimization

Abstract: Community structure identification in complex networks has been an important research topic in recent years. Community detection can be viewed as an optimization problem in which an objective quality function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. In this work Artificial bee colony (ABC) optimization has been used as an effective optimization technique to solve the community detection problem with the advantage that the number of communities is automatically determined in the process. However, the algorithm performance is influenced directly by the quality function used in the optimization process. A comparison is conducted between different popular communities’ quality measures when used as an objective function within ABC. Experiments on real life networks show the capability of the ABC to successfully find an optimized community structure based on the quality function used.

An automatic flower classification approach using machine learning algorithms

Abstract: This work aims to develop an effective flower classification approach using machine learning algorithms. Eight flower categories were analyzed in order to extract their features. Scale Invariant Feature Transform (SIFT) and Segmentation-based Fractal Texture Analysis (SFTA) algorithms are used to extract flower features. The proposed approach consists of three phases namely: segmentation, feature extraction, and classification phases. In segmentation phase, the flower region is segmented to remove the complex background from the images dataset. Then flower image features are extracted. Finally for classification phase, the proposed approach applied Support Vector Machine (SVM) and Random Forests (RF) algorithms to classify different kinds of flowers. An experiment was carried out using the proposed approach on a dataset of 215 flower images. It shows that Support Vector Machine (SVM) based algorithm provides better accuracy compared to the Random Forests (RF) algorithm when using the SIFT as a feature extraction algorithm. While, Random Forests (RF) algorithm provides its better accuracy with SFTA. Moreover, the system is capable of automatically recognize the flower name with a high degree of accuracy.

Retinal Vessel Segmentation Based on Flower Pollination Search Algorithm

Abstract: This paper presents an automated retinal blood vessels segmentation approach based on flower pollination search algorithm (FPSA). The flower pollination search is a new algorithm based on the flower pollination process of flowering plants. The FPSA searches for the optimal clustering of the given retinal image into compact clusters under some constrains. Shape features are used to further enhance the clustering results using local search method. The proposed retinal blood vessels approach is tested on a publicly available databases DRIVE a of retinal images. The results demonstrate that the performance of the proposed approach is comparable with state of the art techniques in terms of accuracy, sensitivity and specificity.

Rough Power Set Tree for Feature Selection and Classification: Case Study on MRI Brain Tumor

Abstract: This article presents a feature selection and classification system for 2D brain tumors from Magnetic resonance imaging (MRI) images. The proposed feature selection and classification approach consists of four main phases. Firstly, clustering phase that applies the K-means clustering algorithm on 2D brain tumors slices. Secondly, feature extraction phase that extracts the optimum feature subset via using the brightness and circularity ratio. Thirdly, reduct generation phase that uses rough set based on power set tree algorithm to choose the reduct. Finally, classification phase that applies Multilayer Perceptron Neural Network algorithm on the reduct. Experimental results showed that the proposed classification approach achieved a high recognition rate compared to other classifiers including Naive Bayes, AD-tree and BF-tree.