Event-Based Soccer Video Retrieval with Interactive Genetic Algorithm

This paper proposes an interactive genetic algorithm (IGA) for soccer video events retrieval with multimodal features. Eight audio-visual features (average shot duration, standard deviation of shot duration, average motion activity, standard deviation of motion activity, average sound energy, standard deviation of sound energy, average speech rate and standard deviation of speech rate) were extracted from each video in database. Then they were encoded as chromosomes and indexed into search table. First, the proposed algorithm randomly selected the videos from the initial population of videos database, and the user selected what he (she) wanted in mind. Next, the associated chromosomes of selected videos were regarded as target chromosomes after crossover and chromosomes in the database videos were compared based on similarity function to obtain the most similar videos as solutions of the next generation. By iterating this process, a new population of videos was retrieved. This approach of retrieval shows about 64% of precision on the average over 300 videos.