Lifelogging is an area of research which is concerned with the capture of many aspects of an individual's life digitally, and within this rapidly emerging field is the significant challenge of managing images passively captured by an individual of their daily life. Possible applications vary from helping those with neurodegenerative conditions recall events from memory, to the maintenance and augmentation of extensive image collections of a tourist's trips. However, a large lifelog of images can quickly amass, with an average of 700,000 images captured each year, using a device such as the SenseCam. We address the problem of managing this vast collection of personal images by investigating automatic techniques that: 1. Identify distinct events within a full day of lifelog images (which typically consists of 2,000 images) e.g. breakfast, working on PC, meeting, etc. 2. Find similar events to a given event in a person's lifelog e.g. "show me other events where I was in the park" 3. Determine those events that are more important or unusual to the user and also select a relevant keyframe image for visual display of an event e.g. a "meeting" is more interesting to review than "working on PC" 4. Augment the images from a wearable camera with higher quality images from external "Web 2.0" sources e.g. find me pictures taken by others of the U2 concert in Croke Park In this dissertation we discuss novel techniques to realise each of these facets and how effective they are. The significance of this work is not only of benefit to the lifelogging community, but also to cognitive psychology researchers studying the potential benefits of lifelogging devices to those with neurodegenerative diseases

Doherty, Aiden R.

English

Lifelogging is an area of research which is concerned with the capture of many aspects of an individual\u27s life digitally, and within this rapidly emerging field is the significant challenge of managing images passively captured by an individual of their daily life. Possible applications vary from helping those with neurodegenerative conditions recall events from memory, to the maintenance and augmentation of extensive image collections of a tourist\u27s trips. However, a large lifelog of images can quickly amass, with an average of 700,000 images captured each year, using a device such as the SenseCam. We address the problem of managing this vast collection of personal images by investigating automatic techniques that: 1. Identify distinct events within a full day of lifelog images (which typically consists of 2,000 images) e.g. breakfast, working on PC, meeting, etc. 2. Find similar events to a given event in a person\u27s lifelog e.g. "show me other events where I was in the park" 3. Determine those events that are more important or unusual to the user and also select a relevant keyframe image for visual display of an event e.g. a "meeting" is more interesting to review than "working on PC" 4. Augment the images from a wearable camera with higher quality images from external "Web 2.0" sources e.g. find me pictures taken by others of the U2 concert in Croke Park In this dissertation we discuss novel techniques to realise each of these facets and how effective they are. The significance of this work is not only of benefit to the lifelogging community, but also to cognitive psychology researchers studying the potential benefits of lifelogging devices to those with neurodegenerative diseases

Irish Universities

Providing effective memory retrieval cues through automatic structuring and augmentation of a lifelog of images

DCU Online Research Access Service

Almost everything we do these days is in some way monitored or logged by computers. We\u27ve come to accept - or maybe just ignore – this massive surveillance because it brings us benefits. In the past we have been easily able to monitor activities on our PC\u27s (e.g. documents we worked
on, e-mails received, etc.), however recently with the advent of numerous types of wearable sensors we have been able to log many more aspects of our activities. While we have the technology to log these activities, we\u27re not really sure what applications we can develop to usefully use this data. One application of lifelogging is as a memory aid to sufferers of neurodegenerative diseases as Alzheimer’s. Indeed in studies on the human mind it has been shown that images are strongly encoded in the brain, and in this seminar we talk about our work in using visual lifelog images to provide effective memory cues. To passively capture these lifelog images we use a wearable camera called the SenseCam. This device captures an average of 2,000 images per day, which equates to over 600,000 images per year! This is too much information for any individual to sift through, therefore we firstly exploit various aspects of the human memory system to break
this data down into manageable chunks or events. Finally in order to provide additional memory cues to an individual (on say a tourist trip), we automatically augment their own lifelog images with images taken from web 2.0 sites such as Flickr. Through analysing the tags of geotagged images on Flickr, we are also automatically able to provide additional augmented material from other sites such as the Yahoo search engine for example. The talk will conclude by talking about future directions that lifelogging may take and the data sources that may drive these changes

Almost everything we do these days is in some way monitored or logged by computers. We've come to accept - or maybe just ignore – this massive surveillance because it brings us benefits. In the past we have been easily able to monitor activities on our PC's (e.g. documents we worked\ud
on, e-mails received, etc.), however recently with the advent of numerous types of wearable sensors we have been able to log many more aspects of our activities. While we have the technology to log these activities, we're not really sure what applications we can develop to usefully use this data. One application of lifelogging is as a memory aid to sufferers of neurodegenerative diseases as Alzheimer’s. Indeed in studies on the human mind it has been shown that images are strongly encoded in the brain, and in this seminar we talk about our work in using visual lifelog images to provide effective memory cues. To passively capture these lifelog images we use a wearable camera called the SenseCam. This device captures an average of 2,000 images per day, which equates to over 600,000 images per year! This is too much information for any individual to sift through, therefore we firstly exploit various aspects of the human memory system to break\ud
this data down into manageable chunks or events. Finally in order to provide additional memory cues to an individual (on say a tourist trip), we automatically augment their own lifelog images with images taken from web 2.0 sites such as Flickr. Through analysing the tags of geotagged images on Flickr, we are also automatically able to provide additional augmented material from other sites such as the Yahoo search engine for example. The talk will conclude by talking about future directions that lifelogging may take and the data sources that may drive these changes

Name not available

Almost everything we do these days is in some way monitored or logged by computers. We've come to accept - or maybe just ignore – this massive surveillance because it brings us benefits. In the past we have been easily able to monitor activities on our PC's (e.g. documents we worked

on, e-mails received, etc.), however recently with the advent of numerous types of wearable sensors we have been able to log many more aspects of our activities. While we have the technology to log these activities, we're not really sure what applications we can develop to usefully use this data. One application of lifelogging is as a memory aid to sufferers of neurodegenerative diseases as Alzheimer’s. Indeed in studies on the human mind it has been shown that images are strongly encoded in the brain, and in this seminar we talk about our work in using visual lifelog images to provide effective memory cues. To passively capture these lifelog images we use a wearable camera called the SenseCam. This device captures an average of 2,000 images per day, which equates to over 600,000 images per year! This is too much information for any individual to sift through, therefore we firstly exploit various aspects of the human memory system to break

this data down into manageable chunks or events. Finally in order to provide additional memory cues to an individual (on say a tourist trip), we automatically augment their own lifelog images with images taken from web 2.0 sites such as Flickr. Through analysing the tags of geotagged images on Flickr, we are also automatically able to provide additional augmented material from other sites such as the Yahoo search engine for example. The talk will conclude by talking about future directions that lifelogging may take and the data sources that may drive these changes

Providing effective memory retrieval cues through automatic structuring and augmentation of a lifelog of images

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Irish Universities

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