Multichannel Texture Analysis Using Localized Spatial Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
A Six-Stimulus Theory for Stochastic Texture
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Color texture measurement and segmentation
Signal Processing - Special section on content-based image and video retrieval
Creating Efficient Codebooks for Visual Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Exploring temporal consistency for video analysis and retrieval
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
How many high-level concepts will fill the semantic gap in news video retrieval?
Proceedings of the 6th ACM international conference on Image and video retrieval
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
Video diver: generic video indexing with diverse features
Proceedings of the international workshop on Workshop on multimedia information retrieval
Combining Face Detection and Novelty to Identify Important Events in a Visual Lifelog
CITWORKSHOPS '08 Proceedings of the 2008 IEEE 8th International Conference on Computer and Information Technology Workshops
Investigating keyframe selection methods in the novel domain of passively captured visual lifelogs
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Automatically Segmenting LifeLog Data into Events
WIAMIS '08 Proceedings of the 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services
Comparing compact codebooks for visual categorization
Computer Vision and Image Understanding
An examination of a large visual lifelog
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
MediAssist: using content-based analysis and context to manage personal photo collections
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
SenseCam: a retrospective memory aid
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
A probabilistic framework for semantic video indexing, filtering,and retrieval
IEEE Transactions on Multimedia
Green multimedia: informing people of their carbon footprint through two simple sensors
Proceedings of the international conference on Multimedia
Aggregating semantic concepts for event representation in lifelogging
Proceedings of the International Workshop on Semantic Web Information Management
Passively recognising human activities through lifelogging
Computers in Human Behavior
Beyond shot retrieval: searching for broadcast news items using language models of concepts
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Dynamic two-stage image retrieval from large multimedia databases
Information Processing and Management: an International Journal
Unconscious emotions: quantifying and logging something we are not aware of
Personal and Ubiquitous Computing
Proceedings of the 6th International Conference on Rehabilitation Engineering & Assistive Technology
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The Microsoft SenseCam is a small lightweight wearable camera used to passively capture photos and other sensor readings from a user's day-to-day activities. It captures on average 3,000 images in a typical day, equating to almost 1 million images per year. It can be used to aid memory by creating a personal multimedia lifelog, or visual recording of the wearer's life. However the sheer volume of image data captured within a visual lifelog creates a number of challenges, particularly for locating relevant content. Within this work, we explore the applicability of semantic concept detection, a method often used within video retrieval, on the domain of visual lifelogs. Our concept detector models the correspondence between low-level visual features and high-level semantic concepts (such as indoors, outdoors, people, buildings, etc.) using supervised machine learning. By doing so it determines the probability of a concept's presence. We apply detection of 27 everyday semantic concepts on a lifelog collection composed of 257,518 SenseCam images from 5 users. The results were evaluated on a subset of 95,907 images, to determine the accuracy for detection of each semantic concept. We conducted further analysis on the temporal consistency, co-occurance and relationships within the detected concepts to more extensively investigate the robustness of the detectors within this domain.