The number of lecture videos available is increasing rapidly, though there is still insufficient accessibility and traceability of lecture video contents. Specifically, it is very desirable to enable people to navigate and access specific slides or topics within lecture videos. To this end, this paper presents the ATLAS system for the VideoLectures.NET challenge (MediaMixer, transLectures) to automatically perform the temporal segmentation and annotation of lecture videos. ATLAS has two main novelties: (i) a SVMhmm model is proposed to learn temporal transition cues and (ii) a fusion scheme is suggested to combine transition cues extracted from heterogeneous information of lecture videos. According to our initial experiments on videos provided by VideoLectures.NET, the proposed algorithm is able to segment and annotate knowledge structures based on fusing temporal transition cues and the evaluation results are very encouraging, which confirms the effectiveness of our ATLAS system.