Semi-Supervised Ridge Regression with Adaptive Graph-Based Label Propagation

Yi, Yugen; Chen, Yuqi; Dai, Jiangyan; Gui, Xiaolin; Chen, Chunlei; Lei, Gang; Wang, Wenle

doi:10.3390/app8122636

Cited by 5 publications

(2 citation statements)

References 48 publications

(70 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the future, Locality Sensitive Hashing [46] and feature selection [47,48,49,50] will be considered to cut down the computation complexity and memory consumption. Meanwhile, Query Expansion (QE) [51] and Graph Fusion (GF) [52] will be integrated into the image retrieval system to retrieve more target images.…”

Section: Discussionmentioning

confidence: 99%

Local Parallel Cross Pattern: A Color Texture Descriptor for Image Retrieval

Feng

Hao

Sbert

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

Riding the wave of visual sensor equipment (e.g., personal smartphones, home security cameras, vehicle cameras, and camcorders), image retrieval (IR) technology has received increasing attention due to its potential applications in e-commerce, visual surveillance, and intelligent traffic. However, determining how to design an effective feature descriptor has been proven to be the main bottleneck for retrieving a set of images of interest. In this paper, we first construct a six-layer color quantizer to extract a color map. Then, motivated by the human visual system, we design a local parallel cross pattern (LPCP) in which the local binary pattern (LBP) map is amalgamated with the color map in “parallel” and “cross” manners. Finally, to reduce the computational complexity and improve the robustness to image rotation, the LPCP is extended to the uniform local parallel cross pattern (ULPCP) and the rotation-invariant local parallel cross pattern (RILPCP), respectively. Extensive experiments are performed on eight benchmark datasets. The experimental results validate the effectiveness, efficiency, robustness, and computational complexity of the proposed descriptors against eight state-of-the-art color texture descriptors to produce an in-depth comparison. Additionally, compared with a series of Convolutional Neural Network (CNN)-based models, the proposed descriptors still achieve competitive results.

show abstract

Section: Discussionmentioning

confidence: 99%

Local Parallel Cross Pattern: A Color Texture Descriptor for Image Retrieval

Feng

Hao

Sbert

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…This forces the learning algorithm to not only fit the data, but also keep the model weights smaller in magnitude [39]. The objective function of RR is defined as follows [40]:…”

Section: Flexibility Predictionmentioning

confidence: 99%

Quantification of the Flexibility of Residential Prosumers

2021

View full text Add to dashboard Cite

Balancing in a distributed generation network is an increasingly difficult task because of the increasing number of residential prosumers on the power network. This paper proposes a framework for the estimation, as well as the prediction of the power flexibility of residential prosumers. In order to quantify the residential buildings’ demand flexibility, a thermoelectric simulation model of a typical residential house was developed based on first engineering principles. Based on the calculated flexibility values, a simple prediction method was used to give a short-term forecast of the prosumer flexibility. The results were validated by simulation experiments incorporating real data for four different scenarios.

show abstract