Video browsing using brightness data

Abstract

This paper presents methods for video browsing and extracting information from video by measuring brightness data. Cut breaks are detected by measuring areas in which inter-frame difference occurs. We propose a video browsing tool (VBToo1) that creates a compressed video containing all important scenes. We utilize two factors related to pixel brightness to allow the automatic generation of the compressed video. . INTRODUCTIONThere have been many attempts at using video in computer and communication fields.2 However, there has been little improvement in the handling of video itself. Video cameras are ubiquitous but the information recorded on the tape is useless until someone looks at it and indicates the tapes's contents. Video information is difficult to handle because no machine can automatically "recognize" scenes with any degree of accuracy. Moreover, there are far more people with cameras producing video than the people capable of editing that video. We think it is necessary to convert video to make it easier to handle.The first stage of our study was an attempt to supply additional information for easy handling. This paper presents a method for extracting information relating to video content This information can then be used for video browsing. In section 2, we discuss the extraction method focusing on cut detection. In section 3, we propose a useful video browsing method as an example of how to utilize the information. The result of experiments are presented in section 4. EXTRACTION OF VIDEO CONTENT Extracting information from videoIt is useful to get information relative to video contents when you see, search, or edit videos. For example, knowing the location of cut breaks and converting video images into smaller units makes video easier to handle. For these reasons, we have to find characteristic values for cut breaks. Besides cut location, there is a lot of other useful information for handling. If the amount of motion and camera operation in a video are known, for instance contents can be recognized in a short time. The location of a slowmotion scene in an action movie is often indicates important or climactical scenes.To know this information, we use brightness data, because the brightness component is an essential value of images and it is well reflected in the video contents. We introduce two brightness values to extract different video content: frame-base histogram difference (FHD), and a pixel-base inter-frame difference histogram (IDH). (Fig. I) FHBrightness distribution is related to the image, and if images change. brightness distribution changes. We refer to the brightness histogram within a frame as FH(frame-base histogram). We can obtain information about moving images from the FH pattern. For example, at the beginning of cuts, FH changes discontinuously at the frame. (see Fig. 2 (a)) 980 / SPIE Vol. 1606 Visual Communications arid Image Processing '91: Image Processing o.9194ij7437/97/$4yJ Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/02/2015 Terms of Use: http://spied...

Shot-boundary detection: unraveled and resolved?

“…A modification of this technique is only counting the pixels that change considerably from one frame to another [20]. Here, the absolute intensity change is compared with the pre-specified threshold , and is only considerable if it exceeds that threshold, that is with if else.…”

“…Most authors work with heuristically chosen global thresholds [4], [18], [20]. An alternative is given in [30], where first the statistical distribution of discontinuity values within a shot is measured.…”

“…8(a) indicates that a good analytic estimate for this distribution and so for the likelihood function can be found in the family of functions given as (19) Using the similar principle of global shape matching, the distribution in Fig. 8(b) and so the likelihood function can best be modeled using a Gaussian function (20) The most suitable parameter combinations and are then found experimentally, such that the rate of detection mistakes for the training sequences is minimized. In other words, we applied the models (19) and (20) in the detection rule (15) for various parameter combinations and selected those combinations for which the detection performance on our training sequences was optimal.…”

“…8(b) and so the likelihood function can best be modeled using a Gaussian function (20) The most suitable parameter combinations and are then found experimentally, such that the rate of detection mistakes for the training sequences is minimized. In other words, we applied the models (19) and (20) in the detection rule (15) for various parameter combinations and selected those combinations for which the detection performance on our training sequences was optimal. The optimal parameter triplet found for the model (19) is (1.33, 4, 2) and the optimal parameter pair for the model (20) was found as (42, 10).…”

Features Extraction and Selection Based on Rough Set and SVM in Abrupt Shot Detection

“…A shot boundary is declared if the difference exceeds a certain threshold. Pixelor block-based temporal image difference [2]- [3], or difference of gray and color histograms [4]- [5] were supposed to be the measure metric. Histograms are robust to object motion.…”