Recognizing human activities from videos is a fundamental research problem in computer vision. Recently, there has been a growing interest in analyzing human behavior from data collected with wearable cameras. First-person cameras continuously record several hours of their wearers' life. To cope with this vast amount of unlabeled and heterogeneous data, novel algorithmic solutions are required. In this paper, we propose a multitask clustering framework for activity of daily living analysis from visual data gathered from wearable cameras. Our intuition is that, even if the data are not annotated, it is possible to exploit the fact that the tasks of recognizing everyday activities of multiple individuals are related, since typically people perform the same actions in similar environments, e.g., people working in an office often read and write documents). In our framework, rather than clustering data from different users separately, we propose to look for clustering partitions which are coherent among related tasks. In particular, two novel multitask clustering algorithms, derived from a common optimization problem, are introduced. Our experimental evaluation, conducted both on synthetic data and on publicly available first-person vision data sets, shows that the proposed approach outperforms several single-task and multitask learning methods.
Egocentric Daily Activity Recognition via Multitask Clustering / Yan, Yan; Ricci, E.; Subramanian, Ramanathan; Liu, Gaowen; Sebe, Niculae. - In: IEEE TRANSACTIONS ON IMAGE PROCESSING. - ISSN 1057-7149. - 2015, 24:10(2015), pp. 2984-2995.
|Titolo:||Egocentric Daily Activity Recognition via Multitask Clustering|
|Autori:||Yan, Yan; Ricci, E.; Subramanian, Ramanathan; Liu, Gaowen; Sebe, Niculae|
|Titolo del periodico:||IEEE TRANSACTIONS ON IMAGE PROCESSING|
|Anno di pubblicazione:||2015|
|Numero e parte del fascicolo:||10|
|Codice identificativo Scopus:||2-s2.0-84933575429|
|Codice identificativo ISI:||WOS:000356176500003|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1109/TIP.2015.2438540|
|Citazione:||Egocentric Daily Activity Recognition via Multitask Clustering / Yan, Yan; Ricci, E.; Subramanian, Ramanathan; Liu, Gaowen; Sebe, Niculae. - In: IEEE TRANSACTIONS ON IMAGE PROCESSING. - ISSN 1057-7149. - 2015, 24:10(2015), pp. 2984-2995.|
|Appare nelle tipologie:||03.1 Articolo su rivista (Journal article)|
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