2,402 research outputs found
Hierarchical Metric Learning for Optical Remote Sensing Scene Categorization
We address the problem of scene classification from optical remote sensing
(RS) images based on the paradigm of hierarchical metric learning. Ideally,
supervised metric learning strategies learn a projection from a set of training
data points so as to minimize intra-class variance while maximizing inter-class
separability to the class label space. However, standard metric learning
techniques do not incorporate the class interaction information in learning the
transformation matrix, which is often considered to be a bottleneck while
dealing with fine-grained visual categories. As a remedy, we propose to
organize the classes in a hierarchical fashion by exploring their visual
similarities and subsequently learn separate distance metric transformations
for the classes present at the non-leaf nodes of the tree. We employ an
iterative max-margin clustering strategy to obtain the hierarchical
organization of the classes. Experiment results obtained on the large-scale
NWPU-RESISC45 and the popular UC-Merced datasets demonstrate the efficacy of
the proposed hierarchical metric learning based RS scene recognition strategy
in comparison to the standard approaches.Comment: Undergoing revision in GRS
Location recognition over large time lags
Would it be possible to automatically associate ancient pictures to modern ones and create fancy cultural heritage city maps? We introduce here the task of recognizing the location depicted in an old photo given modern annotated images collected from the Internet. We present an extensive analysis on different features, looking for the most discriminative and most robust to the image variability induced by large time lags. Moreover, we show that the described task benefits from domain adaptation
EmoNets: Multimodal deep learning approaches for emotion recognition in video
The task of the emotion recognition in the wild (EmotiW) Challenge is to
assign one of seven emotions to short video clips extracted from Hollywood
style movies. The videos depict acted-out emotions under realistic conditions
with a large degree of variation in attributes such as pose and illumination,
making it worthwhile to explore approaches which consider combinations of
features from multiple modalities for label assignment. In this paper we
present our approach to learning several specialist models using deep learning
techniques, each focusing on one modality. Among these are a convolutional
neural network, focusing on capturing visual information in detected faces, a
deep belief net focusing on the representation of the audio stream, a K-Means
based "bag-of-mouths" model, which extracts visual features around the mouth
region and a relational autoencoder, which addresses spatio-temporal aspects of
videos. We explore multiple methods for the combination of cues from these
modalities into one common classifier. This achieves a considerably greater
accuracy than predictions from our strongest single-modality classifier. Our
method was the winning submission in the 2013 EmotiW challenge and achieved a
test set accuracy of 47.67% on the 2014 dataset
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