Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

https://www.jstage.jst.go.jp/article/jsprs1975/37/6/37_6_39/_pdf

1998 IEEE International Conference on SMCに参加して

In recent years, weeds have been responsible for most agricultural yield losses. To deal with this threat, farmers resort to spraying the fields uniformly with herbicides. This method not only requires huge quantities of herbicides but impacts the environment and human health. One way to reduce the cost and environmental impact is to allocate the right doses of herbicide to the right place and at the right time (precision agriculture). Nowadays, unmanned aerial vehicles (UAVs) are becoming an interesting acquisition system for weed localization and management due to their ability to obtain images of the entire agricultural field with a very high spatial resolution and at a low cost. However, despite significant advances in UAV acquisition systems, the automatic detection of weeds remains a challenging problem because of their strong similarity to the crops. Recently, a deep learning approach has shown impressive results in different complex classification problems. However, this approach needs a certain amount of training data, and creating large agricultural datasets with pixel-level annotations by an expert is an extremely time-consuming task. In this paper, we propose a novel fully automatic learning method using convolutional neuronal networks (CNNs) with an unsupervised training dataset collection for weed detection from UAV images. The proposed method comprises three main phases. First, we automatically detect the crop rows and use them to identify the inter-row weeds. In the second phase, inter-row weeds are used to constitute the training dataset. Finally, we perform CNNs on this dataset to build a model able to detect the crop and the weeds in the images. The results obtained are comparable to those of traditional supervised training data labeling, with differences in accuracy of 1.5% in the spinach field and 6% in the bean field.

Deep Learning with Unsupervised Data Labeling for Weed Detection in Line Crops in UAV Images

A survey of approaches for university course timetabling problem

Fruit recognition is useful for automatic fruit harvesting. Fruit recognition application can reduce or minimize human intervention during fruit harvesting operation. However, in computer vision, fruit recognition is very challenging because of similar shapes, colors and textures among various fruits. Illuminations changes due to weather condition also leads to a challenging task for fruit recognition. Thus, this paper tends to investigate the performance of basic Convolutional Neural Network (CNN), Alexnet and Googlenet in recognizing nine different types of fruits from a publicly available dataset.  The experimental results indicate that all these techniques produce excellent recognition accuracy, but basic CNN achieves the fastest recognition result compared with Alexnet and Googlenet.

/pdf/evaluation-of-cnn-alexnet-and-googlenet-for-fruit-17rpwr2zsm.pdf

Evaluation of CNN, Alexnet and GoogleNet for Fruit Recognition

This research investigates the application of texture features for leaf recognition for herbal plant identification  Malaysia is rich with herbal plants but not many people can identify them and know about their uses   Preservation of the knowledge of these herb plants is important since it enables the general public to gain useful knowledge which they can apply whenever necessary  Leaf image is chosen for plant recognition since it is available and visible all the time   Unlike flowers that are not always available or roots that are not visible and not easy to obtain, leaf is the most abundant type of data available in botanical reference collections  A comparative study has been conducted among three popular texture features that are Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP) and Speeded-Up Robust Features (SURF) with multiclass Support Vector Machine (SVM) classifier  A new leaf dataset has been constructed from ten different herb plants  Experimental results using the new constructed dataset and Flavia, an existing dataset, indicate that HOG and LBP produce similar leaf recognition performance and they are better than SURF

/pdf/leaf-recognition-using-texture-features-for-herbal-plant-4s4bjdoz37.pdf

Leaf Recognition using Texture Features for Herbal Plant Identification

This research investigates the application of Convolutional Neural Network (CNN) for palm oil Fresh Fruit Bunch (FFB) ripeness grading recognition. CNN has become the state-of-the-art technique in computer vision especially in object recognition where the recognition accuracy is very impressive. Even though there is no need for feature extraction in CNN, it requires a large amount of training data. To overcome this limitation, utilising the pre-trained CNN model with transfer learning provides the solution. Thus, this research compares CNN, pre-trained CNN model and hand-crafted feature and classifier approach for palm oil Fresh Fruit Bunch (FFB) ripeness grading recognition. The hand-crafted features are colour moments feature, Fast Retina Keypoint (FREAK) binary feature, and Histogram of Oriented Gradient (HOG) texture feature with Support Vector Machine (SVM) classifier. Images of palm oil FFB with four different levels of ripeness have been acquired, and the results indicate that with a small number of sample data, pre-trained CNN model, AlexNet, outperforms CNN and the hand-crafted feature and classifier approach.

Palm Oil Fresh Fruit Bunch Ripeness Grading Recognition Using Convolutional Neural Network

Predicting students’ academic performance is critical for educational institutions because strategic programs can be planned in improving or maintaining students’ performance during their period of studies in the institutions The academic performance in this study is measured by their cumulative grade point average (CGPA) upon graduating In this study, the students’ demographic profile and the CGPA for the first semester of the undergraduate studies are used as the predictor variable for the students’ academic performance in the under-graduate degree program Three predictive models have been developed, namely, logistic regression, artificial neural network (ANN) and Neuro-fuzzy Performances of all the models were measured using root mean squared error (RMSE) The experiments indicate that Neuro-fuzzy model is better than logistic regression and ANN

Predicting students’ academic achievement: Comparison between logistic regression, artificial neural network, and Neuro-fuzzy

Face recognition is one of the well studied problems by researchers in computer visions.  Among the challenges of this task are the occurrence of different facial expressions like happy or sad, and different views of the images such as front and side views.  This paper experiments a publicly available dataset that consists of 200,000 images of celebrity faces. Deep Learning technique is gaining its popularity in computer vision and this paper applies this technique for face recognition problem.  One of the techniques under deep learning is Convolutional Neural Network (CNN).  There is also pre-trained CNN models that are AlexNet and GoogLeNet, which produce excellent accuracy results.  The experimental results indicate that AlexNet is better than basic CNN and GoogLeNet for face recognition.

/pdf/celebrity-face-recognition-using-deep-learning-5cjzxaq9b8.pdf

Zaidah Ibrahim

Papers

Evaluation of CNN, Alexnet and GoogleNet for Fruit Recognition

Leaf Recognition using Texture Features for Herbal Plant Identification

Palm Oil Fresh Fruit Bunch Ripeness Grading Recognition Using Convolutional Neural Network

Predicting students’ academic achievement: Comparison between logistic regression, artificial neural network, and Neuro-fuzzy

Celebrity Face Recognition using Deep Learning