A Novel Multi-Feature Representation of Images for Heterogeneous IoTs

Yu, Li; Feng, Lin; Chen, Chen; Qiu, Tie; Li, Li; Wu, Jun

doi:10.1109/access.2016.2607841

Cited by 14 publications

(8 citation statements)

References 34 publications

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“…We applied graph signal processing to the 2010-2013 New York City taxi data [4]. We first discussed the signal extraction step that allows us to characterize taxi trajectories in terms of Dijkstra shortest paths computed on 700 million taxi trip records.…”

Section: Discussionmentioning

confidence: 99%

“…Measurements in a set of IoT sensors and mobile devices [1]- [4], number of citations in a scientific collaboration network or social media relations (collaboration graph, or social graph) [5] and interactions between individuals in a ecosystem (ecological networks) [6] are some examples of situations in which the acquired data are intimately related to the topology of the network over which they are defined.…”

Section: Introductionmentioning

confidence: 99%

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Graph Signal Processing in a Nutshell

Ribeiro

Lima

2018

JCIS

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Abstract-The framework of graph signal processing was conceived in the last decade with the ambition of generalizing the tools from classical digital signal processing to the case in which the signal is defined over an irregular structure modelled by a graph. Instead of discrete time -what one would call a regular 1-D domain, in which a signal sample is adjacent to only two neighbors and for any pair of contiguous samples the distance is the same -the signals here are defined over graphs and, therefore, the distance and relations between adjacent samples vary along the signal. For instance, one may consider the temperature signal defined from the data of a sensor mesh network. When creating the tools in such a scenario, many challenges arise even with basic concepts of the classical theory. In this paper, the core ideas of graph signal processing are presented, focusing on the two main frameworks developed along the years, and a couple of examples and applications are shown. We conclude drawing attention to a few of the many open opportunities for further studies in the field.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Graph Signal Processing in a Nutshell

Ribeiro

Lima

2018

JCIS

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“…In this section, we conduct two experiments on Holidays and Corel-1K datasets for image retrieval. For these two datasets, we both employ three image descriptors of MSD [47], Gist [4], and HOC [48] to extract multi-view features for all images. All the methods are conducted to project all samples to the same dimensionality.…”

Section: Image Retrievalmentioning

confidence: 99%

A unified framework based on graph consensus term for multi-view learning

Meng¹,

Feng²,

Guo³

2021

Preprint

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In recent years, multi-view learning technologies for various applications have attracted a surge of interest. Due to more compatible and complementary information from multiple views, existing multi-view methods could achieve more promising performance than conventional single-view methods in most situations. However, there are still no sufficient researches on the unified framework in existing multi-view works. Meanwhile, how to efficiently integrate multi-view information is still full of challenges. In this paper, we propose a novel multi-view learning framework, which aims to leverage most existing graph embedding works into a unified formula via introducing the graph consensus term. In particular, our method explores the graph structure in each view independently to preserve the diversity property of graph embedding methods. Meanwhile, we choose heterogeneous graphs to construct the graph consensus term to explore the correlations among multiple views jointly. To this end, the diversity and complementary information among different views could be simultaneously considered. Furthermore, the proposed framework is utilized to implement the multiview extension of Locality Linear Embedding, named Multi-view Locality Linear Embedding (MvLLE), which could be efficiently solved by applying the alternating optimization strategy. Empirical validations conducted on six benchmark datasets can show the effectiveness of our proposed method.

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“…And for each category, we randomly select 10 images as query ones. And we utilize the three descriptors [32,24,33] to extract features same as above. The demensions of features from these 3 views are 72, 512, 768 respectively.…”

Section: Image Retrievalmentioning

confidence: 99%

Multi-view reconstructive preserving embedding for dimension reduction

et al. 2019

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With the development of feature extraction technique, one sample always can be represented by multiple features which locate in high-dimensional space. Multiple features can reflect various perspectives of one same sample, so there must be compatible and complementary information among the multiple views. Therefore, it's natural to integrate multiple features together to obtain better performance. However, most multi-view dimension reduction methods cannot handle multiple features from nonlinear space with high dimensions. To address this problem, we propose a novel multi-view dimension reduction method named Multi-view Reconstructive Preserving Embedding (MRPE) in this paper. MRPE reconstructs each sample by utilizing its k nearest neighbors. The similarities between each sample and its neighbors are primely mapped into lowerdimensional space in order to preserve the underlying neighborhood structure of the original manifold. MRPE fully exploits correlations between each sample and its neighbors from multiple views by linear reconstruction. Furthermore, MRPE constructs an optimization problem and derives an iterative procedure to obtain the low-dimensional embedding. Various evaluations based on the applications of document classification, face recognition and image retrieval demonstrate the effectiveness of our proposed approach on multi-view dimension reduction.

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A Novel Multi-Feature Representation of Images for Heterogeneous IoTs

Cited by 14 publications

References 34 publications

Graph Signal Processing in a Nutshell

Graph Signal Processing in a Nutshell

A unified framework based on graph consensus term for multi-view learning

Multi-view reconstructive preserving embedding for dimension reduction

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