Keyword Based Keyframe Extraction in Online Video Collections

Keyframe extraction methods aim to find in a video sequence the most significant frames, according to specific criteria. In this paper we propose a new method to search, in a video database, for frames that are related to a given keyword, and to extract the best ones, according to a proposed quality factor. We first exploit a speech to text algorithm to extract automatic captions from all the video in a specific domain database. Then we select only those sequences (clips), whose captions include a given keyword, thus discarding a lot of information that is useless for our purposes. Each retrieved clip is then divided into shots, using a video segmentation method, that is based on the SURF descriptors and keypoints. The sentence of the caption is projected onto the segmented clip, and we select the shot that includes the input keyword. The selected shot is further inspected to find good quality and stable parts, and the frame which maximizes a quality metric is selected as the best and the most significant frame. We compare the proposed algorithm with another keyframe extraction method based on local features, in terms of Significance and Quality

A Literature Study On Video Retrieval Approaches

A detailed survey has been carried out to identify the various research articles available in the literature in all the categories of video retrieval and to do the analysis of the major contributions and their advantages, following are the literature used for the assessment of the state-of-art work on video retrieval. Here, a large number of papershave been studied

Scene Segmentation and Classification

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

A Study On Information Retrieval Systems

A video is a key component of today's multimedia applications,  including Video Cassette Recording (VCR), Video-on-Demand (VoD), and virtual walkthrough. This happens supplementary with the fast amplification in video skill (Rynson W.H. Lau et al. 2000). Owing to innovation's progress in the  media, computerized TV, and data frameworks, an immense measure of video information is now exhaustively realistic (Walid G. Aref et al. 2003). The startling advancement in computerized video content has made entrée and moves the data in a tremendous video database a muddled and sensible issue (Chih-Wen Su et al. 2005). Therefore, the necessity for creating devices and frameworks that can effectively investigate the most needed video content, has evoked a great deal of interest among analysts. Sports video has been chosen as the prime application in this proposition since it has attracted viewers around the world