3 research outputs found
Multiplication fusion of sparse and collaborative-competitive representation for image classification
Representation based classification methods have become a hot research topic
during the past few years, and the two most prominent approaches are sparse
representation based classification (SRC) and collaborative representation
based classification (CRC). CRC reveals that it is the collaborative
representation rather than the sparsity that makes SRC successful.
Nevertheless, the dense representation of CRC may not be discriminative which
will degrade its performance for classification tasks. To alleviate this
problem to some extent, we propose a new method called sparse and
collaborative-competitive representation based classification (SCCRC) for image
classification. Firstly, the coefficients of the test sample are obtained by
SRC and CCRC, respectively. Then the fused coefficient is derived by
multiplying the coefficients of SRC and CCRC. Finally, the test sample is
designated to the class that has the minimum residual. Experimental results on
several benchmark databases demonstrate the efficacy of our proposed SCCRC. The
source code of SCCRC is accessible at https://github.com/li-zi-qi/SCCRC.Comment: submitted to International Journal of Machine Learning and
Cybernetic
HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification
Hyperspectral image (HSI) classification is widely used for the analysis of
remotely sensed images. Hyperspectral imagery includes varying bands of images.
Convolutional Neural Network (CNN) is one of the most frequently used deep
learning based methods for visual data processing. The use of CNN for HSI
classification is also visible in recent works. These approaches are mostly
based on 2D CNN. Whereas, the HSI classification performance is highly
dependent on both spatial and spectral information. Very few methods have
utilized the 3D CNN because of increased computational complexity. This letter
proposes a Hybrid Spectral Convolutional Neural Network (HybridSN) for HSI
classification. Basically, the HybridSN is a spectral-spatial 3D-CNN followed
by spatial 2D-CNN. The 3D-CNN facilitates the joint spatial-spectral feature
representation from a stack of spectral bands. The 2D-CNN on top of the 3D-CNN
further learns more abstract level spatial representation. Moreover, the use of
hybrid CNNs reduces the complexity of the model compared to 3D-CNN alone. To
test the performance of this hybrid approach, very rigorous HSI classification
experiments are performed over Indian Pines, Pavia University and Salinas Scene
remote sensing datasets. The results are compared with the state-of-the-art
hand-crafted as well as end-to-end deep learning based methods. A very
satisfactory performance is obtained using the proposed HybridSN for HSI
classification. The source code can be found at
\url{https://github.com/gokriznastic/HybridSN}.Comment: Published in IEEE Geoscience and Remote Sensing Letter
Multi-local Collaborative AutoEncoder
The excellent performance of representation learning of autoencoders have
attracted considerable interest in various applications. However, the structure
and multi-local collaborative relationships of unlabeled data are ignored in
their encoding procedure that limits the capability of feature extraction. This
paper presents a Multi-local Collaborative AutoEncoder (MC-AE), which consists
of novel multi-local collaborative representation RBM (mcrRBM) and multi-local
collaborative representation GRBM (mcrGRBM) models. Here, the Locality
Sensitive Hashing (LSH) method is used to divide the input data into
multi-local cross blocks which contains multi-local collaborative relationships
of the unlabeled data and features since the similar multi-local instances and
features of the input data are divided into the same block. In mcrRBM and
mcrGRBM models, the structure and multi-local collaborative relationships of
unlabeled data are integrated into their encoding procedure. Then, the local
hidden features converges on the center of each local collaborative block.
Under the collaborative joint influence of each local block, the proposed MC-AE
has powerful capability of representation learning for unsupervised clustering.
However, our MC-AE model perhaps perform training process for a long time on
the large-scale and high-dimensional datasets because more local collaborative
blocks are integrate into it. Five most related deep models are compared with
our MC-AE. The experimental results show that the proposed MC-AE has more
excellent capabilities of collaborative representation and generalization than
the contrastive deep models