Video shot boundary detection and key-frame extraction using mathematical models. (Détection des changements de plans et extraction d'images représentatives dans une séquence vidéo)

With the recent advancement in multimedia technologies, in conjunction with the rapid increase of the volume of digital video data and the growth of internet ; it has becom mandatory to have the hability browse and search through information stored in large multimedia databases. For this purpose, content based video retrieval (CBVR) has become an active area of research durinf the last decade. The objective of this thesis is to present applications for temporal video segmentation and video retrieval based on different mathematical models. A shot is considered as the elementary unit of a video, and is defined as a continuous sequence of frames taken from a single camera, representing an action during time. The different types of transitions that may occur in a video sequence are categorized into : abrupt and gradual transition. In this work, through statistical analysis, we segment a video into its constituent units. This is achieved by identifying transitions between adjacent shots. The first proposed algorithm aims to detect abrupt shot transitions only by measuring the similarity between consecutive frames. Given the size of the vector containing distances, it can be modeled by a log normal distribution since all the values are positive. Gradual shot transition identification is a more difficult task when compared to cut detection. Generally, a gradual transition may share similar characteristics as a dynamic segment with camera or object motion. In this work, singular value decomposition (SVD) is performed to project features from the spatial domain to the singular space. Resulting features are reduced and more refined, which makes the remaining tasks easier. The proposed system, designed for detecting both abrupt and gradual transitions, has lead to reliable performances achieving high detection rates. In addition, the acceptable computational time allows to process in real time. Once a video is partitioned into its elementary units, high-level applications can be processed, such as the key-frame extraction. Selecting representative frames from each shot to form a storyboard is considered as a static and local video summarization. In our research, we opted for a global method based on local extraction. Using refined centrist features from the singular space, we select representative frames using modified k-means clustering based on important scenes. This leads to catch pertinent frames without redoudancy in the final storyboard.