19,097 research outputs found
Action Recognition by Hierarchical Mid-level Action Elements
Realistic videos of human actions exhibit rich spatiotemporal structures at
multiple levels of granularity: an action can always be decomposed into
multiple finer-grained elements in both space and time. To capture this
intuition, we propose to represent videos by a hierarchy of mid-level action
elements (MAEs), where each MAE corresponds to an action-related spatiotemporal
segment in the video. We introduce an unsupervised method to generate this
representation from videos. Our method is capable of distinguishing
action-related segments from background segments and representing actions at
multiple spatiotemporal resolutions. Given a set of spatiotemporal segments
generated from the training data, we introduce a discriminative clustering
algorithm that automatically discovers MAEs at multiple levels of granularity.
We develop structured models that capture a rich set of spatial, temporal and
hierarchical relations among the segments, where the action label and multiple
levels of MAE labels are jointly inferred. The proposed model achieves
state-of-the-art performance in multiple action recognition benchmarks.
Moreover, we demonstrate the effectiveness of our model in real-world
applications such as action recognition in large-scale untrimmed videos and
action parsing
3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks
Human activity understanding with 3D/depth sensors has received increasing
attention in multimedia processing and interactions. This work targets on
developing a novel deep model for automatic activity recognition from RGB-D
videos. We represent each human activity as an ensemble of cubic-like video
segments, and learn to discover the temporal structures for a category of
activities, i.e. how the activities to be decomposed in terms of
classification. Our model can be regarded as a structured deep architecture, as
it extends the convolutional neural networks (CNNs) by incorporating structure
alternatives. Specifically, we build the network consisting of 3D convolutions
and max-pooling operators over the video segments, and introduce the latent
variables in each convolutional layer manipulating the activation of neurons.
Our model thus advances existing approaches in two aspects: (i) it acts
directly on the raw inputs (grayscale-depth data) to conduct recognition
instead of relying on hand-crafted features, and (ii) the model structure can
be dynamically adjusted accounting for the temporal variations of human
activities, i.e. the network configuration is allowed to be partially activated
during inference. For model training, we propose an EM-type optimization method
that iteratively (i) discovers the latent structure by determining the
decomposed actions for each training example, and (ii) learns the network
parameters by using the back-propagation algorithm. Our approach is validated
in challenging scenarios, and outperforms state-of-the-art methods. A large
human activity database of RGB-D videos is presented in addition.Comment: This manuscript has 10 pages with 9 figures, and a preliminary
version was published in ACM MM'14 conferenc
An empirical study of inter-concept similarities in multimedia ontologies
Generic concept detection has been a widely studied topic in recent research on multimedia analysis and retrieval, but the issue of how to exploit the structure of a multimedia ontology as well as different inter-concept relations, has not received similar attention. In this paper, we present results from our empirical analysis of different types of similarity among semantic concepts in two multimedia ontologies, LSCOM-Lite and CDVP-206. The results show promise that the proposed methods may be helpful in providing insight into the existing inter-concept relations within an ontology and selecting the most facilitating set of concepts and hierarchical relations. Such an analysis as this can be utilized in various tasks such as building more reliable concept detectors and designing large-scale ontologies
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