687 research outputs found
Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation
Remote sensing (RS) image retrieval is of great significant for geological
information mining. Over the past two decades, a large amount of research on
this task has been carried out, which mainly focuses on the following three
core issues: feature extraction, similarity metric and relevance feedback. Due
to the complexity and multiformity of ground objects in high-resolution remote
sensing (HRRS) images, there is still room for improvement in the current
retrieval approaches. In this paper, we analyze the three core issues of RS
image retrieval and provide a comprehensive review on existing methods.
Furthermore, for the goal to advance the state-of-the-art in HRRS image
retrieval, we focus on the feature extraction issue and delve how to use
powerful deep representations to address this task. We conduct systematic
investigation on evaluating correlative factors that may affect the performance
of deep features. By optimizing each factor, we acquire remarkable retrieval
results on publicly available HRRS datasets. Finally, we explain the
experimental phenomenon in detail and draw conclusions according to our
analysis. Our work can serve as a guiding role for the research of
content-based RS image retrieval
Objects2action: Classifying and localizing actions without any video example
The goal of this paper is to recognize actions in video without the need for
examples. Different from traditional zero-shot approaches we do not demand the
design and specification of attribute classifiers and class-to-attribute
mappings to allow for transfer from seen classes to unseen classes. Our key
contribution is objects2action, a semantic word embedding that is spanned by a
skip-gram model of thousands of object categories. Action labels are assigned
to an object encoding of unseen video based on a convex combination of action
and object affinities. Our semantic embedding has three main characteristics to
accommodate for the specifics of actions. First, we propose a mechanism to
exploit multiple-word descriptions of actions and objects. Second, we
incorporate the automated selection of the most responsive objects per action.
And finally, we demonstrate how to extend our zero-shot approach to the
spatio-temporal localization of actions in video. Experiments on four action
datasets demonstrate the potential of our approach
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