45,095 research outputs found
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
Improving Person Re-identification by Attribute and Identity Learning
Person re-identification (re-ID) and attribute recognition share a common
target at learning pedestrian descriptions. Their difference consists in the
granularity. Most existing re-ID methods only take identity labels of
pedestrians into consideration. However, we find the attributes, containing
detailed local descriptions, are beneficial in allowing the re-ID model to
learn more discriminative feature representations. In this paper, based on the
complementarity of attribute labels and ID labels, we propose an
attribute-person recognition (APR) network, a multi-task network which learns a
re-ID embedding and at the same time predicts pedestrian attributes. We
manually annotate attribute labels for two large-scale re-ID datasets, and
systematically investigate how person re-ID and attribute recognition benefit
from each other. In addition, we re-weight the attribute predictions
considering the dependencies and correlations among the attributes. The
experimental results on two large-scale re-ID benchmarks demonstrate that by
learning a more discriminative representation, APR achieves competitive re-ID
performance compared with the state-of-the-art methods. We use APR to speed up
the retrieval process by ten times with a minor accuracy drop of 2.92% on
Market-1501. Besides, we also apply APR on the attribute recognition task and
demonstrate improvement over the baselines.Comment: Accepted to Pattern Recognition (PR
Local Deep Neural Networks for gender recognition
Deep learning methods are able to automatically discover better representations of the data to improve the performance of the classifiers. However, in computer vision tasks, such as the gender recognition problem, sometimes it is difficult to directly learn from the entire image. In this work we propose a new model called Local Deep Neural Network (Local-DNN), which is based on two key concepts: local features and deep architectures. The model learns from small overlapping regions in the visual field using discriminative feed forward networks with several layers. We evaluate our approach on two well-known gender benchmarks, showing that our Local-DNN outperforms other deep learning methods also evaluated and obtains state-of-the-art results in both benchmarks. (C) 2015 Elsevier B.V. All rights reserved.This work was financially supported by the Ministerio de Ciencia e Innovacin (Spain), Plan Nacional de I-D+i, TEC2009-09146, and the FPI grant BES-2010-032945.Mansanet Sandín, J.; Albiol Colomer, A.; Paredes Palacios, R. (2016). Local Deep Neural Networks for gender recognition. Pattern Recognition Letters. 70:80-86. https://doi.org/10.1016/j.patrec.2015.11.015S80867
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