Abstract The emergence of low-cost high-quality personal wearable cameras combined with the increasing storage capacity of video-sharing websites have evoked a growing interest in first-person videos, since most videos are composed of long-running unedited streams which are usually tedious and unpleasant to watch. State-of-the-art semantic fast-forward methods currently face the challenge of providing an adequate balance between smoothness in visual flow and the emphasis on the relevant parts. In this work, we present the Multi-Importance Fast-Forward (MIFF), a fully automatic methodology to fast-forward egocentric videos facing these challenges. The dilemma of defining what is the semantic information of a video is addressed by a learning process based on the preferences of the user. Results show that the proposed method keeps over 3 times more semantic content than the state-of-the-art fast-forward. Finally, we discuss the need of a particular video stabilization technique for fast-forward egocentric videos 1 .
Unlike our previous works, in this work, we use a multi-importance approach in our splitting strategy to segment the video temporally. In other words, rather than labeling the segments as semantic or non-semantic, we define multiple levels for the semantic segments. Thus, the segments can be emphasized according to their importance. We also seek the automation of some steps. We use the Particle Swarm Optimization (PSO) @cite_0 algorithm to better select the weights for the speed-ups definition and the transition costs of the frames. Furthermore, we remove the need for an existing classifier to determine the semantic score.
@cite_0
2152195021
A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.