By Guangyu Gao, Chi Harold Liu
The arrival of the electronic age has created the necessity to be ready to shop, deal with, and digitally use an ever-increasing quantity of video and audio fabric. hence, video cataloguing has emerged as a demand of the days. Video Cataloguing: constitution Parsing and content material Extraction explains tips to successfully practice video constitution research in addition to extract the fundamental semantic contents for video summarization, that is crucial for dealing with large-scale video data.
This e-book addresses the problems of video cataloguing, together with video constitution parsing and simple semantic observe extraction, quite for motion picture and teleplay movies. It starts off by means of offering readers with a primary realizing of video constitution parsing. It examines video shot boundary detection, fresh examine on video scene detection, and uncomplicated principles for semantic observe extraction, together with video textual content acceptance, scene reputation, and personality identification.
The ebook lists and introduces essentially the most prevalent positive factors in video research. It introduces and analyzes the preferred shot boundary detection tools and in addition offers fresh learn on motion picture scene detection as one other vital and important step for video cataloguing, video indexing, and retrieval.
The authors suggest a strong motion picture scene reputation process in response to a breathtaking body and consultant function patch. They describe tips to realize characters in video clips and television sequence adequately and successfully in addition to easy methods to use those personality names as cataloguing goods for an clever catalogue.
The booklet proposes a fascinating program of spotlight extraction in basketball movies and concludes by way of demonstrating the way to layout and enforce a prototype process of computerized motion picture and teleplay cataloguing (AMTC) according to the techniques brought within the book.
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Additional info for Video Cataloguing: Structure Parsing and Content Extraction
Sample text
More details of a typical feature learning are shown in [39]. In [39], the random subpatches are extracted from unlabeled input images at first, and each patch has dimension ω × ω and d channels (ω is referred to as the receptive field size). Each ω × ω patch can be represented as a vector in R N of pixel intensity values, with N = w · w · d, and then a dataset of m randomly sampled patches, X = x (1) , . . , x (m) , where x (i) ∈ R N . Given the dataset, each patch x (i ) is normalized by subtracting the mean and dividing by the standard deviation of its elements.
These samples are then accumulated into orientation histograms summarizing the contents over 4 × 4 subregions, as shown on the right, with the length of each arrow corresponding to the sum of the gradient magnitudes near that direction within the region. This figure shows a 2 × 2 descriptor array computed from an 8 × 8 set of samples, whereas the experiments in this chapter use 4 × 4 descriptors computed from a 16 × 16 sample array. Generally, we use a 4 × 4 × 8 = 128 descriptor. 3 Gabor Feature A Gabor filter, named after Dennis Gabor, is a linear filter used for edge detection.
2 Scale-Invariant Feature Transform Scale-invariant feature transform (SIFT) is nearly the most popular local feature of the past 10 years; it was published by David Lowe in 1999 [124, 125]. Lowe’s method for image feature generation transforms an image into a large collection of feature vectors, each of which is invariant to image translation, scaling, and rotation; partially invariant to illumination changes; and robust to local geometric distortion. SIFT is invariant to image scale and rotation and robust to affine distortion, change in three-dimensional (3D) viewpoint, addition of noise, and change in illumination.