Iterative sparsity score for feature selection and its extension for multimodal data

Zu, Chen; Zhu, Linling; Zhang, Daoqiang

doi:10.1016/j.neucom.2016.08.124

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…After calculating the score for each feature, they are sorted in the ascending order of SparseScore to select the relevant ones. In the classification experiments, Liu et al have demonstrated that this score outperforms other methods in most cases, especially for multi-class problems [36].…”

Section: Feature Selectionmentioning

confidence: 99%

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Vehicle logo recognition using histograms of oriented gradient descriptor and sparsity score

2020

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Most of vehicle have the similar structures and designs. It is extremely complicated and difficult to identify and classify vehicle brands based on their structure and shape. As we require a quick and reliable response, so vehicle logos are an alternative method of determining the type of a vehicle. In this paper, we propose a method for vehicle logo recognition based on feature selection method in a hybrid way. Vehicle logo images are first characterized by Histograms of Oriented Gradient descriptors and the final features vector are then applied feature selection method to reduce the irrelevant information. Moreover, we release a new benchmark dataset for vehicle logo recognition and retrieval task namely, VLR-40. The experimental results are evaluated on this database which show the efficiency of the proposed approach.

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Section: Feature Selectionmentioning

confidence: 99%

“…Liu et al extend the unsupervised sparsity score to supervised context by utilizing the class label information [36,37]. Let denotes the ℎ feature of ℎ instance in class , ̂ is the element of sparse similarity matrix which is constructed within the class , is a Ndimensional vector with =1, if belongs to the class and 0 otherwise.…”

Section: Feature Selectionmentioning

confidence: 99%