149,171 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
Learning a Pose Lexicon for Semantic Action Recognition
This paper presents a novel method for learning a pose lexicon comprising
semantic poses defined by textual instructions and their associated visual
poses defined by visual features. The proposed method simultaneously takes two
input streams, semantic poses and visual pose candidates, and statistically
learns a mapping between them to construct the lexicon. With the learned
lexicon, action recognition can be cast as the problem of finding the maximum
translation probability of a sequence of semantic poses given a stream of
visual pose candidates. Experiments evaluating pre-trained and zero-shot action
recognition conducted on MSRC-12 gesture and WorkoutSu-10 exercise datasets
were used to verify the efficacy of the proposed method.Comment: Accepted by the 2016 IEEE International Conference on Multimedia and
Expo (ICME 2016). 6 pages paper and 4 pages supplementary materia
Exemplar Based Deep Discriminative and Shareable Feature Learning for Scene Image Classification
In order to encode the class correlation and class specific information in
image representation, we propose a new local feature learning approach named
Deep Discriminative and Shareable Feature Learning (DDSFL). DDSFL aims to
hierarchically learn feature transformation filter banks to transform raw pixel
image patches to features. The learned filter banks are expected to: (1) encode
common visual patterns of a flexible number of categories; (2) encode
discriminative information; and (3) hierarchically extract patterns at
different visual levels. Particularly, in each single layer of DDSFL, shareable
filters are jointly learned for classes which share the similar patterns.
Discriminative power of the filters is achieved by enforcing the features from
the same category to be close, while features from different categories to be
far away from each other. Furthermore, we also propose two exemplar selection
methods to iteratively select training data for more efficient and effective
learning. Based on the experimental results, DDSFL can achieve very promising
performance, and it also shows great complementary effect to the
state-of-the-art Caffe features.Comment: Pattern Recognition, Elsevier, 201
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
table
A Discriminative Representation of Convolutional Features for Indoor Scene Recognition
Indoor scene recognition is a multi-faceted and challenging problem due to
the diverse intra-class variations and the confusing inter-class similarities.
This paper presents a novel approach which exploits rich mid-level
convolutional features to categorize indoor scenes. Traditionally used
convolutional features preserve the global spatial structure, which is a
desirable property for general object recognition. However, we argue that this
structuredness is not much helpful when we have large variations in scene
layouts, e.g., in indoor scenes. We propose to transform the structured
convolutional activations to another highly discriminative feature space. The
representation in the transformed space not only incorporates the
discriminative aspects of the target dataset, but it also encodes the features
in terms of the general object categories that are present in indoor scenes. To
this end, we introduce a new large-scale dataset of 1300 object categories
which are commonly present in indoor scenes. Our proposed approach achieves a
significant performance boost over previous state of the art approaches on five
major scene classification datasets
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