Machine learning for Internet of Things data analysis: A survey

Rapid developments in hardware, software, and communication technologies have allowed the emergence of Internet-connected sensory devices that provide observation and data measurement from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As the numbers grow and technologies become more mature, the volume of data published will increase. Internet-connected devices technology, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interaction between the physical and cyber worlds. In addition to increased volume, the IoT generates Big Data characterized by velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this Big Data is the key to developing smart IoT applications. This article assesses the different machine learning methods that deal with the challenges in IoT data by considering smart cities as the main use case. The key contribution of this study is presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying Support Vector Machine (SVM) on Aarhus Smart City traffic data is presented for a more detailed exploration.Comment: Digital Communications and Networks (2017

Machine Learning for Internet of Things Data Analysis: A Survey

Rapid developments in hardware, software, and communication technologies have facilitated the emergence of Internet-connected sensory devices that provide observations and data measurements from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As these numbers grow and technologies become more mature, the volume of data being published will increase. The technology of Internet-connected devices, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interactions between the physical and cyber worlds. In addition to an increased volume, the IoT generates big data characterized by its velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this big data are the key to developing smart IoT applications. This article assesses the various machine learning methods that deal with the challenges presented by IoT data by considering smart cities as the main use case. The key contribution of this study is the presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying a Support Vector Machine (SVM) to Aarhus smart city traffic data is presented for a more detailed exploration

Organising a photograph collection based on human appearance

This thesis describes a complete framework for organising digital photographs in an unsupervised manner, based on the appearance of people captured in the photographs. Organising a collection of photographs manually, especially providing the identities of people captured in photographs, is a time consuming task. Unsupervised grouping of images containing similar persons makes annotating names easier (as a group of images can be named at once) and enables quick search based on query by example. The full process of unsupervised clustering is discussed in this thesis. Methods for locating facial components are discussed and a technique based on colour image segmentation is proposed and tested. Additionally a method based on the Principal Component Analysis template is tested, too. These provide eye locations required for acquiring a normalised facial image. This image is then preprocessed by a histogram equalisation and feathering, and the features of MPEG-7 face recognition descriptor are extracted. A distance measure proposed in the MPEG-7 standard is used as a similarity measure. Three approaches to grouping that use only face recognition features for clustering are analysed. These are modified k-means, single-link and a method based on a nearest neighbour classifier. The nearest neighbour-based technique is chosen for further experiments with fusing information from several sources. These sources are context-based such as events (party, trip, holidays), the ownership of photographs, and content-based such as information about the colour and texture of the bodies of humans appearing in photographs. Two techniques are proposed for fusing event and ownership (user) information with the face recognition features: a Transferable Belief Model (TBM) and three level clustering. The three level clustering is carried out at “event” level, “user” level and “collection” level. The latter technique proves to be most efficient. For combining body information with the face recognition features, three probabilistic fusion methods are tested. These are the average sum, the generalised product and the maximum rule. Combinations are tested within events and within user collections. This work concludes with a brief discussion on extraction of key images for a representation of each cluster

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p

Learning Feature Selection and Combination Strategies for Generic Salient Object Detection

For a diverse range of applications in machine vision from social media searches to robotic home care providers, it is important to replicate the mechanism by which the human brain selects the most important visual information, while suppressing the remaining non-usable information. Many computational methods attempt to model this process by following the traditional model of visual attention. The traditional model of attention involves feature extraction, conditioning and combination to capture this behaviour of human visual attention. Consequently, the model has inherent design choices at its various stages. These choices include selection of parameters related to the feature computation process, setting a conditioning approach, feature importance and setting a combination approach. Despite rapid research and substantial improvements in benchmark performance, the performance of many models depends upon tuning these design choices in an ad hoc fashion. Additionally, these design choices are heuristic in nature, thus resulting in good performance only in certain settings. Consequentially, many such models exhibit low robustness to difficult stimuli and the complexities of real-world imagery. Machine learning and optimisation technique have long been used to increase the generalisability of a system to unseen data. Surprisingly, artificial learning techniques have not been investigated to their full potential to improve generalisation of visual attention methods. The proposed thesis is that artificial learning can increase the generalisability of the traditional model of visual attention by effective selection and optimal combination of features. The following new techniques have been introduced at various stages of the traditional model of visual attention to improve its generalisation performance, specifically on challenging cases of saliency detection: 1. Joint optimisation of feature related parameters and feature importance weights is introduced for the first time to improve the generalisation of the traditional model of visual attention. To evaluate the joint learning hypothesis, a new method namely GAOVSM is introduced for the tasks of eye fixation prediction. By finding the relationships between feature related parameters and feature importance, the developed method improves the generalisation performance of baseline method (that employ human encoded parameters). 2. Spectral matting based figure-ground segregation is introduced to overcome the artifacts encountered by region-based salient object detection approaches. By suppressing the unwanted background information and assigning saliency to object parts in a uniform manner, the developed FGS approach overcomes the limitations of region based approaches. 3. Joint optimisation of feature computation parameters and feature importance weights is introduced for optimal combination of FGS with complementary features for the first time for salient object detection. By learning feature related parameters and their respective importance at multiple segmentation thresholds and by considering the performance gaps amongst features, the developed FGSopt method improves the object detection performance of the FGS technique also improving upon several state-of-the-art salient object detection models. 4. The introduction of multiple combination schemes/rules further extends the generalisability of the traditional attention model beyond that of joint optimisation based single rules. The introduction of feature composition based grouping of images, enables the developed IGA method to autonomously identify an appropriate combination strategy for an unseen image. The results of a pair-wise ranksum test confirm that the IGA method is significantly better than the deterministic and classification based benchmark methods on the 99% confidence interval level. Extending this line of research, a novel relative encoding approach enables the adapted XCSCA method to group images having similar saliency prediction ability. By keeping track of previous inputs, the introduced action part of the XCSCA approach enables learning of generalised feature importance rules. By more accurate grouping of images as compared with IGA, generalised learnt rules and appropriate application of feature importance rules, the XCSCA approach improves upon the generalisation performance of the IGA method. 5. The introduced uniform saliency assignment and segmentation quality cues enable label free evaluation of a feature/saliency map. By accurate ranking and effective clustering, the developed DFS method successfully solves the complex problem of finding appropriate features for combination (on an-image-by-image basis) for the first time in saliency detection. The DFS method enables ground truth free evaluation of saliency methods and advances the state-of-the-art in data driven saliency aggregation by detection and deselection of redundant information. The final contribution is that the developed methods are formed into a complete system where analysis shows the effects of their interactions on the system. Based on the saliency prediction accuracy versus computational time trade-off, specialised variants of the proposed methods are presented along with the recommendations for further use by other saliency detection systems. This research work has shown that artificial learning can increase the generalisation of the traditional model of attention by effective selection and optimal combination of features. Overall, this thesis has shown that it is the ability to autonomously segregate images based on their types and subsequent learning of appropriate combinations that aid generalisation on difficult unseen stimuli