Content vs. context for multimedia semantics: the case of SenseCam image structuring

Much of the current work on determining multimedia semantics from multimedia artifacts is based around using either context, or using content. When leveraged thoroughly these can independently provide content description which is used in building content-based applications. However, there are few cases where multimedia semantics are determined based on an integrated analysis of content and context. In this keynote talk we present one such example system in which we use an integrated combination of the two to automatically structure large collections of images taken by a SenseCam, a device from Microsoft Research which passively records a person’s daily activities. This paper describes the post-processing we perform on SenseCam images in order to present a structured, organised visualisation of the highlights of each of the wearer’s days

Image annotation with Photocopain

Photo annotation is a resource-intensive task, yet is increasingly essential as image archives and personal photo collections grow in size. There is an inherent conflict in the process of describing and archiving personal experiences, because casual users are generally unwilling to expend large amounts of effort on creating the annotations which are required to organise their collections so that they can make best use of them. This paper describes the Photocopain system, a semi-automatic image annotation system which combines information about the context in which a photograph was captured with information from other readily available sources in order to generate outline annotations for that photograph that the user may further extend or amend

Smartphone picture organization: a hierarchical approach

We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 40 persons. Experimental results demonstrate major user satisfaction with respect to state of the art solutions in terms of organization.Peer ReviewedPreprin

LifeLogging: personal big data

We have recently observed a convergence of technologies to foster the emergence of lifelogging as a mainstream activity. Computer storage has become significantly cheaper, and advancements in sensing technology allows for the efficient sensing of personal activities, locations and the environment. This is best seen in the growing popularity of the quantified self movement, in which life activities are tracked using wearable sensors in the hope of better understanding human performance in a variety of tasks. This review aims to provide a comprehensive summary of lifelogging, to cover its research history, current technologies, and applications. Thus far, most of the lifelogging research has focused predominantly on visual lifelogging in order to capture life details of life activities, hence we maintain this focus in this review. However, we also reflect on the challenges lifelogging poses to an information retrieval scientist. This review is a suitable reference for those seeking a information retrieval scientist’s perspective on lifelogging and the quantified self

Exploiting linked data to create rich human digital memories

Memories are an important aspect of a person's life and experiences. The area of human digital memories focuses on encapsulating this phenomenon, in a digital format, over a lifetime. Through the proliferation of ubiquitous devices, both people and the surrounding environment are generating a phenomenal amount of data. With all of this disjointed information available, successfully searching it and bringing it together, to form a human digital memory, is a challenge. This is especially true when a lifetime of data is being examined. Linked Data provides an ideal, and novel, solution for overcoming this challenge, where a variety of data sources can be drawn upon to capture detailed information surrounding a given event. Memories, created in this way, contain vivid structures and varied data sources, which emerge through the semantic clustering of content and other memories. This paper presents DigMem, a platform for creating human digital memories, based on device-specific services and the user's current environment. In this way, information is semantically structured to create temporal "memory boxes" for human experiences. A working prototype has been successfully developed, which demonstrates the approach. In order to evaluate the applicability of the system a number of experiments have been undertaken. These have been successful in creating human digital memories and illustrating how a user can be monitored in both indoor and outdoor environments. Furthermore, the user's heartbeat information is analysed to determine his or her heart rate. This has been achieved with the development of a QRS Complex detection algorithm and heart rate calculation method. These methods process collected electrocardiography (ECG) information to discern the heart rate of the user

Personal information management for the elderly

Given the current desire to draw a greater percentage of the elderly population into a significant use of ICT, a reflection is presented on the suitability of computer-based personal information management systems for older people. The paper is presented from the point of view of a computer-literate grandchild trying to demonstrate to a grandparent the benefits of using an electronic system. The main focus of attention is the address book

MyPlaces: detecting important settings in a visual diary

We describe a novel approach to identifying specific settings in large collections of passively captured images corresponding to a visual diary. An algorithm developed for setting detection should be capable of detecting images captured at the same real world locations (e.g. in the dining room at home, in front of the computer in the office, in the park, etc.). This requires the selection and implementation of suitable methods to identify visually similar backgrounds in images using their visual features. We use a Bag of Keypoints approach. This method is based on the sampling and subsequent vector quantization of multiple image patches. The image patches are sampled and described using Scale Invariant Feature Transform (SIFT) features. We compare two different classifiers, K Nearest Neighbour and Multiclass Linear Perceptron, and present results for classifying ten different settings across one week’s worth of images. Our results demonstrate that the method produces good classification accuracy even without exploiting geometric or context based information. We also describe an early prototype of a visual diary browser that integrates the classification results

Exploiting linked data to create rich human digital memories

Memories are an important aspect of a person's life and experiences. The area of human digital memories focuses on encapsulating this phenomenon, in a digital format, over a lifetime. Through the proliferation of ubiquitous devices, both people and the surrounding environment are generating a phenomenal amount of data. With all of this disjointed information available, successfully searching it and bringing it together, to form a human digital memory, is a challenge. This is especially true when a lifetime of data is being examined. Linked Data provides an ideal, and novel, solution for overcoming this challenge, where a variety of data sources can be drawn upon to capture detailed information surrounding a given event. Memories, created in this way, contain vivid structures and varied data sources, which emerge through the semantic clustering of content and other memories. This paper presents DigMem, a platform for creating human digital memories, based on device-specific services and the user's current environment. In this way, information is semantically structured to create temporal "memory boxes" for human experiences. A working prototype has been successfully developed, which demonstrates the approach. In order to evaluate the applicability of the system a number of experiments have been undertaken. These have been successful in creating human digital memories and illustrating how a user can be monitored in both indoor and outdoor environments. Furthermore, the user's heartbeat information is analysed to determine his or her heart rate. This has been achieved with the development of a QRS Complex detection algorithm and heart rate calculation method. These methods process collected electrocardiography (ECG) information to discern the heart rate of the user. This information is essential in illustrating how certain situations can make the user feel. (C) 2013 Elsevier B.V. All rights reserved.

Public ubiquitous computing systems:lessons from the e-campus display deployments

In this paper we reflect on our experiences of deploying ubiquitous computing systems in public spaces and present a series of lessons that we feel will be of benefit to researchers planning similar public deployments. We focus on experiences gained from building and deploying three experimental public display systems as part of the e-campus pro ject. However, we believe the lessons are likely to be generally applicable to many different types of public ubicomp deployment

Organising and structuring a visual diary using visual interest point detectors

As wearable cameras become more popular, researchers are increasingly focusing on novel applications to manage the large volume of data these devices produce. One such application is the construction of a Visual Diary from an individual’s photographs. Microsoft’s SenseCam, a device designed to passively record a Visual Diary and cover a typical day of the user wearing the camera, is an example of one such device. The vast quantity of images generated by these devices means that the management and organisation of these collections is not a trivial matter. We believe wearable cameras, such as SenseCam, will become more popular in the future and the management of the volume of data generated by these devices is a key issue. Although there is a significant volume of work in the literature in the object detection and recognition and scene classification fields, there is little work in the area of setting detection. Furthermore, few authors have examined the issues involved in analysing extremely large image collections (like a Visual Diary) gathered over a long period of time. An algorithm developed for setting detection should be capable of clustering images captured at the same real world locations (e.g. in the dining room at home, in front of the computer in the office, in the park, etc.). This requires the selection and implementation of suitable methods to identify visually similar backgrounds in images using their visual features. We present a number of approaches to setting detection based on the extraction of visual interest point detectors from the images. We also analyse the performance of two of the most popular descriptors - Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF).We present an implementation of a Visual Diary application and evaluate its performance via a series of user experiments. Finally, we also outline some techniques to allow the Visual Diary to automatically detect new settings, to scale as the image collection continues to grow substantially over time, and to allow the user to generate a personalised summary of their data