Scene Segmentation and Classification

In this thesis work we propose a novel method for video segmentation and classification, which are\ud important tasks in indexing and retrieval of videos. Video indexing techniques requires the video\ud to be segmented effectively into smaller meaningful units shots. Because of huge volumes of digital\ud data and their dimensionality, indexing the data in shot level is a tough task. Scene classification\ud has become a challenging and important problem in recent years because of its efficiency in video\ud indexing. The main issue in video segmentation is the selection of features that are robust to false\ud illuminations and object motion. Shot boundary detection algorithm is proposed which detects both\ud the abrupt and gradual transitions simultaneously. Each shot is represented using a key-frame(s).\ud The key-frame is a still image of a shot or it is a cumulative histogram representation that best\ud represents the content of a shot. From each shot one or multiple key frame(s) are extracted. This\ud research work presents a new method for segmenting videos into scenes. Scene is defined as a sequence\ud of shots that are semantically co-related.\ud Shots from a scene will have similar color content, background information. The similarity\ud between a pair of shots is the color histogram intersection of the key frames of the two shots. Histogram\ud intersection outputs the count of pixels with similar color in the two frames. Shot similarity\ud matrix with 0\ud ′\ud s and 1\ud ′\ud s is computed, that outputs the similarity between any two shots. Shots are\ud from the same scene if the similarity between the two shots is 1, else they are from different scenes.\ud Spectral clustering algorithm is used to identify scene boundaries. Shots belonging to scene will\ud form a cluster. A new method is proposed to detect scenes, sequence of shots that are similar will\ud have an edge between them and forms a node. Edge represents the similarity value 1 between shots.\ud SVM classifier is used for scene classification. The experimental results on different data-sets shows\ud that the proposed algorithms can effectively segment and classify digital videos.\ud Key words: Content based video retrieval, video content analysis, video indexing, shot boundary\ud detection, key-frames, scene segmentation, and video classification