Video segmentation using a histogram-based fuzzy c-means clustering algorithm

The purpose of video segmentation is to segment video sequence into shots where each shot represents a sequence of frames having the same contents, and then select key frames from each shot for indexing. Existing video segmentation methods can be classified into two groups: the shot change detection (SCD) approach for which thresholds have to be pre-assigned, and the clustering approach for which a prior knowledge of the number of clusters is required. In this paper, we propose a video segmentation method using a histogram-based fuzzy c-means (HBFCM) clustering algorithm. This algorithm is a hybrid of the two approaches aforementioned, and is designed to overcome the drawbacks of both approaches. The HBFCM clustering algorithm is composed of three phases: the feature extraction phase, the clustering phase, and the key-frame selection phase. In the first phase, differences between color histogram are extracted as features. In the second phase, the fuzzy c-means (FCM) is used to group features into three clusters: the shot change (SC) cluster, the suspected shot change (SSC) cluster, and the no shot change (NSC) cluster. In the last phase, shot change frames are identified from the SC and the SSC, and then used to segment video sequences into shots. Finally, key frames are selected from each shot. Simulation results indicate that the HBFCM clustering algorithm is robust and applicable to various types of video sequences.     

Early fire detection method in video for vessels

New generation vessels are equipped with fire detecting sensors; however, fire may not immediately be detected if it is far away from the sensors. The fire process therefore cannot be recorded. A video-based fire alarm system is developed to overcome the drawbacks of traditional fire detection equipment. This paper presents a video-based flame and smoke detection method for vessels. For flame detection, the dominant flame color lookup table (DFCLT) is created by using the fuzzy c-means clustering algorithm. The changed video frames are automatically selected and the changed regions deduced from these frames. An elementary, medium, or emergency flame alarm is then triggered by comparing the pixels of changed regions with the DFCLT. The changed video frames are automatically selected for smoke detection. The changed regions are deduced from these frames. If the shape of the changed region conforms to the characteristic which the top area is wider than the bottom area, a dangerous smoke alarm is sounded. The experimental results show that the proposed fire detection approach can detect dangerous flames and smoke, effectively and efficiently.