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1,321 results on '"Dagan Feng"'

195. Similarity Based Block Sparse Subset Selection for Video Summarization

Author

David Dagan Feng, Shaohui Mei, Mingyang Ma, Shuai Wan, Zhiyong Wang, and Mohammed Bennamoun

Subjects
Selection (relational algebra), business.industry, Computer science, Feature vector, Frame (networking), Pattern recognition, Automatic summarization, Kernel (linear algebra), Similarity (network science), Media Technology, Artificial intelligence, Electrical and Electronic Engineering, business, Neural coding, Sparse matrix
Abstract

Video summarization (VS) is generally formulated as a subset selection problem where a set of representative keyframes or key segments is selected from an entire video frame set. Though many sparse subset selection based VS algorithms have been proposed in the past decade, most of them adopt linear sparse formulation in the explicit feature vector space of video frames, and don’t consider the local or global relationships among frames. In this paper, we first extend the conventional sparse subset selection for VS into kernel block sparse subset selection (KBS3) to utilize the advantage of kernel sparse coding and introduce a local inter-frame relationship through packing of frame blocks. Going a step further, we propose a similarity based block sparse subset selection (SB2S3) model by applying a specially designed transformation matrix on the KBS3 model in order to introduce a kind of global inter-frame relationship through the similarity. Finally, a greedy pursuit based algorithm is devised for the proposed NP-hard model optimization. The proposed SB2S3 has the following advantages: 1) through the similarity between each frame and any other frame, the global relationship among all frames can be considered; 2) through block sparse coding, the local relationship of adjacent frames is further considered; and 3) it has a wider application, since features can derive similarity, but not vice versa. It is believed that the effect of modeling such global and local relationships among frames in this paper, is similar to that of modeling the long-range and short-range dependencies among frames in deep learning based methods. Experimental results on three benchmark datasets have demonstrated that the proposed approach is superior to not only other sparse subset selection based VS methods but also most unsupervised deep-learning based VS methods.

Published
2021

196. Kernelized Mahalanobis Distance for Fuzzy Clustering

Author

Zuyin Xiao, Xiangjun Duan, Sen Zeng, Shan Zeng, Xiuying Wang, and David Dagan Feng

Subjects
Mahalanobis distance, Fuzzy clustering, Computer science, business.industry, Applied Mathematics, Pattern recognition, 02 engineering and technology, Fuzzy logic, Euclidean distance, Kernel (linear algebra), Statistical classification, ComputingMethodologies_PATTERNRECOGNITION, Computational Theory and Mathematics, Artificial Intelligence, Control and Systems Engineering, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, Cluster analysis, business, Curse of dimensionality
Abstract

Data samples of complicated geometry and nonlinear separability are considered as common challenges to clustering algorithms. In this article, we first construct Mahalanobis distance in the kernel space and then propose a novel fuzzy clustering model with a kernelized Mahalanobis distance, namely KMD-FC. The key contributions of KMD-FC include: first, the construction of KMD matrix is innovatively transformed from the Euclidean distance kernel matrix, which is able to effectively avoid the problem of “curse of dimensionality” posed by explicitly calculating the sample covariance matrix in the kernel space; second, for the first time, the kernelized Gustafson–Kessel (GK) fuzzy C-means algorithm is achieved, which is critically important to extend the applications of the GK algorithm to the nonlinear classification tasks; finally, taking account of the overall distribution of samples in the kernel space after kernel mapping to improve the generalizability of the proposed KMD-FC clustering method. Comprehensive experiments conducted on a wide range of datasets, including synthetic datasets and machine learning repository (UCI) datasets, have validated that the proposed clustering algorithm outperformed the state-of-the-art methods in comparison.

Published
2021

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