E2LSH based multiple kernel approach for object detection

Zhang, Ruijie; Wei, Fushan; Li, Bicheng

doi:10.1016/j.neucom.2013.07.027

Cited by 10 publications

(4 citation statements)

References 24 publications

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“…Jiao et al [9] utilized E 2 LSH to mining image information, which had a good performance. Zhang et al [28] proposed an improved multi kernel learning method based on E 2 LSH and applied it in image information analysis. Li et al [10] took the advantage of E 2 LSH to optimize the application of large-scale document index.…”

Section: E 2 Lshmentioning

confidence: 99%

NE-UserCF: Collaborative Filtering Recommender System Model based on NMF and E2LSH

Yun¹

2017

IJPE

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With the rapid development of big data and cloud computing, recommender systems (RSs) have gained significant attention in recent decades. However, there are still many challenges and drawbacks existed in RSs, such as complex and high-dimensional data, low recommendation accuracy, time-consuming and low-efficiency, which to a large extent restrict its applications. Non-negative Matrix Factorization algorithm (NMF) is a matrix factorization algorithm which finds the positive factorization of a given positive matrix. It can eliminate invalid and redundant features in user-rating matrix (URM), reduce URM's dimension. Exact Euclidean Locality Sensitive Hashing (E 2 LSH) is an advanced algorithm for solving the approximate or exact Near Neighbor Search in high dimensional spaces. It can cluster similar-interest users (SIUs) of URM efficiently. Therefore, the authors propose an improved recommender system model named NE-UserCF (NMF-E 2 LSH-UserCF) based on NMF and E 2 LSH to improve the quality and performance of recommendation. The authors first utilize the NMF to process original URM, get a new-URM without invalid and redundant features. Then use E 2 LSH to cluster users in new-URM based on their interests and produce the similar-interest-user matrix (SIUM). The authors further process the Top-10 recommendations by adopting the user-based collaborative filtering algorithm (UserCF). Finally evaluate experimental results by analyzing metrics Precision, Recall, Coverage and Popularity. Experiments indicate that NE-UserCF proposed in this paper improves the quality of recommendation and has a good performance.

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Section: E 2 Lshmentioning

confidence: 99%

NE-UserCF: Collaborative Filtering Recommender System Model based on NMF and E2LSH

Yun¹

2017

IJPE

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“…In [10] it was shown that this reduced time complexity could also be achieved by a KD-forest approximation. Zhang [11] proposed an E2LSH-MKL based dictionary construction method. This method utilizes nonlinear combination of multiple different kernels in order to make full use of information generated from the nonlinear interaction of different kernels.…”

Section: Introductionmentioning

confidence: 99%

“…This method utilizes nonlinear combination of multiple different kernels in order to make full use of information generated from the nonlinear interaction of different kernels. The method in [11] can demonstrate superior time and space efficiency over K-Means and HKM or AKM, in both theory and practice. But, the hash functions in [11] are randomly generated without the prior information of the training data.…”

Section: Introductionmentioning

confidence: 99%

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Object Classification based on Weakly Supervised E2LSH and Saliency Map Weighting

2016

KSII TIIS

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The most popular approach in object classification is based on the bag of visual-words model, which has several fundamental problems that restricting the performance of this method, such as low time efficiency, the synonym and polysemy of visual words, and the lack of spatial information between visual words. In view of this, an object classification based on weakly supervised E2LSH and saliency map weighting is proposed. Firstly, E2LSH (Exact Euclidean Locality Sensitive Hashing) is employed to generate a group of weakly randomized visual dictionary by clustering SIFT features of the training dataset, and the selecting process of hash functions is effectively supervised inspired by the random forest ideas to reduce the randomcity of E2LSH. Secondly, graph-based visual saliency (GBVS) algorithm is applied to detect the saliency map of different images and weight the visual words according to the saliency prior. Finally, saliency map weighted visual language model is carried out to accomplish object classification. Experimental results datasets of Pascal 2007 and Caltech-256 indicate that the distinguishability of objects is effectively improved and our method is superior to the state-of-the-art object classification methods.

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Patch-Based Visual Tracking with Two-Stage Multiple Kernel Learning

Fan

Xiang

2015

Lecture Notes in Computer Science

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E2LSH based multiple kernel approach for object detection

Cited by 10 publications

References 24 publications

NE-UserCF: Collaborative Filtering Recommender System Model based on NMF and E2LSH

NE-UserCF: Collaborative Filtering Recommender System Model based on NMF and E2LSH

Object Classification based on Weakly Supervised E2LSH and Saliency Map Weighting

Patch-Based Visual Tracking with Two-Stage Multiple Kernel Learning

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