An Improved Pca Fusion Method Based on Generalized Intensity–Hue–Saturation Fusion Technique

Ren, Dong; Liu, Yanmei; Yang, Xiaodong; Wang, Jihua; Yu, Haiyang

doi:10.1080/10798587.2008.10643320

Cited by 6 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hannon et al [17] unified collaborative filtering and content-based filtering. In the reference [12], Jahrer combined different models (SVD, KNN, Restricted Boltzmann machine, Effects) to minimize the prediction error [24]. Xue et al [14] proposed a framework…”

Section: Related Workmentioning

confidence: 99%

An Effective Collaborative Filtering Via Enhanced Similarity and Probability Interval Prediction

Zou¹,

Wang²,

Wei³

et al. 2014

Intelligent Automation & Soft Computing

View full text Add to dashboard Cite

In recent years, as one of the most successful recommendation methods, collaborative filtering has been widely used in the recommendation system. Collaborative filtering predicts the active user preference for goods or services by collecting a historical data set of users' ratings for items; the underlying assumption is that the active user will prefer those items that the similar users prefer. Usually the data is quit sparse, which makes the computation of similarity between users or items imprecise and consequently reduces the accuracy of recommendations. In this paper, we propose an enhanced similarity method that the common ratings and the all ratings are both taken into account. Additionally, we present a generative probabilistic prediction framework in which we first predict a missing data probability value interval instead of a certain value by using the defined range of similar neighbors' ratings, and the final missing data rating is produced in the interval. Empirical studies on two datasets (MovieLens and Netflix) show that the proposed algorithm consistently outperforms other state-of-theart collaborative filtering algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

An Effective Collaborative Filtering Via Enhanced Similarity and Probability Interval Prediction

Zou¹,

Wang²,

Wei³

et al. 2014

Intelligent Automation & Soft Computing

View full text Add to dashboard Cite

show abstract

“…Of rarely achieved, and the results tend to have parallax error and ghosting. To improve results, many research efforts focus on devising better alignment [4] or compositing techniques [5].…”

Section: Introductionmentioning

confidence: 99%

Image Stitching Method Based on Projective Interpolation

Zhang¹,

Ren²,

Huang³

et al. 2016

International Journal of Robotics and Automation

Self Cite

View full text Add to dashboard Cite

Parallax error and ghosting are serious problems in unmanned aerial vehicle (UAV) image stitching. These problems occur even more obviously when stitching together large-parallax images. In this paper, an image stitching method based on projective interpolation is proposed. First, we estimate the homography matrix from the UAV images, obtain its inverse matrix. Second, the two matrices are decomposed and interpolated into sub-matrices. Then these submatrices are composed into two new matrices. Finally, the image stitching is finished by warping images using the new matrices.The proposed method disperses the parallax into the two images to help reduce the parallax error. Moreover, the method introduces a seam cutting technique to preserve the geometric structure and further improve local alignment. We conducted three experiments that compared the proposed method with other state-of-the-art methods. The experimental results show that the proposed method can effectively reduce parallax error, eliminate ghosting, and achieve an overall good stitching result.

show abstract

A review of fusion methods of multi-spectral image

Bai

2015

Optik

View full text Add to dashboard Cite

An Improved Pca Fusion Method Based on Generalized Intensity–Hue–Saturation Fusion Technique

Cited by 6 publications

References 6 publications

An Effective Collaborative Filtering Via Enhanced Similarity and Probability Interval Prediction

An Effective Collaborative Filtering Via Enhanced Similarity and Probability Interval Prediction

Image Stitching Method Based on Projective Interpolation

A review of fusion methods of multi-spectral image

Contact Info

Product

Resources

About