This document discusses automatic detection of foods and activities in lifelogging images. It describes the MyFood project for automatic monitoring of eating habits using lifelogging images. MyFood could help people track their diets and raise awareness of eating behaviors. The document also discusses the NTCIR-12 Lifelogging evaluation campaign, the first to evaluate tasks related to lifelogging data like images and location data. The campaign involved a test collection of lifelogging documents and sample queries to evaluate systems' ability to retrieve relevant documents. The best performing system in the campaign was from Laboratoire D’Informatique de Grenoble.

1. DETECTING FOOD AND ACTIVITIES
IN LIFELOGGING IMAGES
MyFood Automatic Monitoring of Eating Habits
NTCIR: The first Lifelogging evaluation campaign
• Bahjat Safadi: Laboratoire d’Informatique de Grenoble
• Rami Albatal: HeyStaks, ex. Insight Centre for Data Analytic
Bahjat.Safadi@imag.fr rami.albatal@heystaks.com
@ramialbatal

2. MYFOOD
AUTOMATIC MONITORING OF EATING HABITS
IE Commercialisation Feasibility By
Rami Albatal
Collaborators
Cathal Gurrin, Bahjat Safadi

3. Rami Albatal
WHY
• Because there is a need for an easy way of writing and
maintaining reliable and objective food diary.
• This helps people in getting aware of their eating habits,
which leads into changes in eating behaviour and
enhancing health conditions and an improved quality of life.
• But current solutions for food diary provide only manual input
and monitoring services.
An automatic solution for eating tracking will save time and
provide more objective food diary.
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4. Rami Albatal
THE POTENTIAL
• Healthcare Specialists / Nutritionists
• MyFood automatically shows what food is taken and provides a
visual diary of food and eating habits (quantity, quality,
ingredients, frequency… etc.)
• Slimming and Wellbeing
• MyFood can offer a hassle free and objective food diary
solution. It allows also greater awareness by capturing photos
for the personal food consumption.
• Food Market-Research
• MyFood could detect consumed food and how the context of
consumptions in real-life settings (at work, in a social
environment, on table, while walking, indoor, outdoor…etc.).
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5. NTCIR12-LIFELOG
FIRST LIFELOGGING EVALUATION CAMPAIGN
Organisers
Cathal Gurrin, Rami Albatal, Liting Zhou (DCU, Ireland)
Hideo Joho (Tsukuba University, Japan)
Frank Hopfgartner (University of Glasgow, UK)
Best Performance
MRIM Team (Laboratoire D’Informatique de Grenoble, France)
Lead Participant: Bahjat Safadi

6. Rami Albatal
NTCIR12-LIFELOG
• First Lifelog test collection
• Components
• A collection of domain-representative documents
(timestamp + images + semantic locations + movement +
deep learning classification output).
• A set of queries (called topics) that are representative of
the domain of application.
• A set of relevance judgments that may be complete or
incomplete (pooled) and will identify the documents that
are relevant to each query.
• Privacy is an essential feature
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7. Rami Albatal
NTCIR12-LIFELOG
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Autographer Moves App

8. Rami Albatal
NTCIR12-LIFELOG
Example of a Topic (query)
TITLE: Tower Bridge
DESCRIPTION: Find the moment(s) when I was
looking at Tower Bridge in London.
NARRATIVE: To be considered relevant, the full
span of Tower Bridge must be visible.
Moments of crossing the Tower Bridge
or showing some subset of Tower
Bridge are not considered relevant.
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9. Rami Albatal
THANKS!
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