Deep learning for visual understanding: A review
Deep learning algorithms are a subset of the machine learning algorithms, which aim at
discovering multiple levels of distributed representations. Recently, numerous deep learning …
discovering multiple levels of distributed representations. Recently, numerous deep learning …
Constructing a prior-dependent graph for data clustering and dimension reduction in the edge of AIoT
Abstract The Artificial Intelligence Internet of Things (AIoT) is an emerging concept aiming to
perceive, understand and connect the 'intelligent things' to make the intercommunication of …
perceive, understand and connect the 'intelligent things' to make the intercommunication of …
Feature learning using spatial-spectral hypergraph discriminant analysis for hyperspectral image
Hyperspectral image (HSI) contains a large number of spatial-spectral information, which
will make the traditional classification methods face an enormous challenge to discriminate …
will make the traditional classification methods face an enormous challenge to discriminate …
Laplacian regularized low-rank representation and its applications
Low-rank representation (LRR) has recently attracted a great deal of attention due to its
pleasing efficacy in exploring low-dimensional subspace structures embedded in data. For a …
pleasing efficacy in exploring low-dimensional subspace structures embedded in data. For a …
Click prediction for web image reranking using multimodal sparse coding
Image reranking is effective for improving the performance of a text-based image search.
However, existing reranking algorithms are limited for two main reasons: 1) the textual meta …
However, existing reranking algorithms are limited for two main reasons: 1) the textual meta …
Bit-scalable deep hashing with regularized similarity learning for image retrieval and person re-identification
Extracting informative image features and learning effective approximate hashing functions
are two crucial steps in image retrieval. Conventional methods often study these two steps …
are two crucial steps in image retrieval. Conventional methods often study these two steps …
Region-based saliency detection and its application in object recognition
The objective of this paper is twofold. First, we introduce an effective region-based solution
for saliency detection. Then, we apply the achieved saliency map to better encode the image …
for saliency detection. Then, we apply the achieved saliency map to better encode the image …
Sparse modeling for image and vision processing
In recent years, a large amount of multi-disciplinary research has been conducted on sparse
models and their applications. In statistics and machine learning, the sparsity principle is …
models and their applications. In statistics and machine learning, the sparsity principle is …
Image-based three-dimensional human pose recovery by multiview locality-sensitive sparse retrieval
Image-based 3-D human pose recovery is usually conducted by retrieving relevant poses
with image features. However, it suffers from the high dimensionality of image features and …
with image features. However, it suffers from the high dimensionality of image features and …
Hyper-Laplacian regularized multilinear multiview self-representations for clustering and semisupervised learning
In this paper, we address the multiview nonlinear subspace representation problem.
Traditional multiview subspace learning methods assume that the heterogeneous features …
Traditional multiview subspace learning methods assume that the heterogeneous features …