Video sequence matching via decision tree path following

Video Comparison Using Tree Matching Algorithms

This paper proposes a video structure matching framework based on a tree matching algorithm which can be used as an effective video retrieval tool in a media management system. We define two types of specialized tree matching algorithms. The first is the non-ordered tree matching and the second is the ordered tree matching. The first algorithm is not constrained by the temporal ordering of the ...

A Path Following Algorithm for Graph Matching

Branching Path Following for Graph Matching

Recently, graph matching algorithms utilizing the path following strategy have exhibited state-of-the-art performances. However, the paths computed in these algorithms often contain singular points, which usually hurt the matching performance. To deal with this issue, in this paper we propose a novel path following strategy, named branching path following (BPF), which consequently improves grap...

Path Following with Adaptive Path Estimation for Graph Matching

Graph matching plays an important role in many fields in computer vision. It is a well-known general NP-hard problem and has been investigated for decades. Among the large amount of algorithms for graph matching, the algorithms utilizing the path following strategy exhibited state-of-art performances. However, the main drawback of this category of algorithms lies in their high computational bur...

Clustering Via Decision Tree Construction

Clustering is an exploratory data analysis task. It aims to find the intrinsic structure of data by organizing data objects into similarity groups or clusters. It is often called unsupervised learning because no class labels denoting an a priori partition of the objects are given. This is in contrast with supervised learning (e.g., classification) for which the data objects are already labeled ...