results

Multi-View Video Summarization using Bipartite Matching Constrained Optimum-Path Forest Clustering

We evaluate five Multi-view videos namely Office1, Campus, OfficeLobby, Road and Badminton, capturing both indoor and outdoor environments . All the five video datasets are typical surveillance videos, consisting of three to four views. Detailed information regarding these Multi-View videos and ground truths of videos were prepared and made available at http://cs.nju.edu.cn/ywguo/summarization.html We use these available ground truths of videos to validate the performance of our method.

1.Comparison with Standard K-means clustering Algorithm.

First we use a simple base method where K-means clustering algorithm is applied to visual features (color, edge and shape) to obtain the Multi-view summary. We choose K-means because of its low computational overhead in clustering of high dimensional data.

Result of Multi-view summary of given datasets by using k-means clustering algorithm are given as [Office1], [Campus],[OfficeLobby].

2.Our Purposed Method.

In this paper, we proposed a novel framework for the Multi-view video summarization using bipartite matching constrained OPF. The problem of capturing the inherent correlation between the multiple views was modeled as a MCMW matching problem in bipartite graphs. We introduced the OPF clustering for summarizing the multi-view videos with very effective results.

Result of Multi-view video summary of given datasets by using our proposed method are given as : [Office1] [Campus] [Office Lobby] [Road] [Badminton].