Hugo Bastos de Paula

A NEW DISSIMILARITY MEASURE FOR CUT DETECTION USING BIPARTITE GRAPH MATCHING

Cut detection is part of the video segmentation problem, and consists in identifying the boundary between two consecutive shots. In this case, when two consecutive frames are similar, they are considered to be in the same shot. This work presents an approach to cut detection using a new simple and efficient dissimilarity measure (which is also invariant to rotation and translation) based on the size of a bipartite graph matching. To establish some parameter values, a machine learning approach is used. Experimental results provides a comparison between the new approach and other popular algorithms from the literature, showing that the new algorithm is robust and has a high performance compared to other methods for cut detection.

Gradual transition detection based on bipartite graph matching approach

This paper addresses gradual transition detection which is part of video segmentation problem, and consists in identifying the boundary between consecutive shots. In this work, we propose an approach to cope with gradual transition detection in which we define and use a new dissimilarity measure based on the size of the maximum cardinality matching calculated using a bipartite graph with respect to a specified window. The experiments have used a video dataset which presents a variety of different video genres with more than 500 gradual transitions and our method with a much simpler classification approach achieves more than 90% recall with almost 80% precision which is similar to the best results found.

A Rotation and Translation Invariant Algorithm for Cut Detection Using Bipartite Graph Matching

Cut detection is part of the video segmentation problem, and consists in the identification of the boundary between consecutive shots. In this case, when two consecutive frames are similar, they are considered to be in the same shot. This work presents an approach to cut detection using a rotation and translation invariant algorithm based on the use of the maximum cardinality of a bipartite graph matching between two frames as the dissimilarity distance. Experimental results provides a comparison between the new approach and other popular algorithms from the literature, showing that the new algorithm is robust and has a high performance if compared to other methods of cut detection.

An efficient access method for multimodal video retrieval

Efficient and effective handling of video documents depends on the availability of indexes. Manual indexing is unfeasible for large video collections. Video combines different types of data from different modalities. Using information from multiple modalities may result in a more robust and accurate video retrieval. Therefore, effective indexing for video retrieval requires a multimodal approach in which either the most appropriate modality is selected or the different modalities are used in collaborative fashion. This paper presents a new metric access method -- Slim2-tree -- which combines information from multiple modalities within a single index structure for video retrieval. Experimental studies on a large real dataset show the video similarity search performance of the proposed technique. Additionally, we present experiments comparing our method against state-of-the-art of multimodal solutions. Comparative test results demonstrate that our technique improves the performance of video similarity queries.