Models for motion-based video indexing and retrieval

Abstract

With the rapid proliferation of multimedia applications that require video data management, it is becoming more desirable to provide proper video data indexing techniques capable of representing the rich semantics in video data. In real-time applications, the need for efficient query processing is another reason for the use of such techniques. We present models that use the object motion information in order to characterize the events to allow subsequent retrieval. Algorithms for different spatiotemporal search cases in terms of spatial and temporal translation and scale invariance have been developed using various signal and image processing techniques. We have developed a prototype video search engine, PICTURESQUE (pictorial information and content transformation unified retrieval engine for spatiotemporal queries) to verify the proposed methods. Development of such technology will enable true multimedia search engines that will enable indexing and searching of the digital video data based on its true content.

A Content Based Video Retrieval Analysis System with Extensive Features by Using Kullback-Leibler

“…Combining Eq. (11) determines the KLD between the feature set of the actual question clip and therefore the feature set of an information video clip. In similar technique, KLD is decided for all the opposite information video clips.…”

“…CBIR contains the following four parts in system realization: data collection, build up feature database, search in the data. Video segmentation splits a video file into shots that is illustrated as Associate in treatment conterminous sequence of video frames that square measure recorded from one camera operation [11]. Edge data primarily based video segmentation, image segmentation video segmentation and alter detection based video segmentation is that the three sorts of Video segmentation [14].…”

Video Analysis and Indexing

“…or temporal (e.g. motion) features or are related to semantic objects (Li & Özsu, 1997) (Dağtas et al, 2000) (Al-Khatib et al, 1999. More sophisticated approaches are either a hierarchical representation of video objects (Analyti & Christodoulakis, 1995) (Yeo & Yeung, 1997) (Kyriakaki, 2000) based on their structure, or an event-based approach that represents a video object as a set of (non-contiguous, even overlapping) video segments called strata or temporal cohesions (Hacid et al, 2000) that correspond to individual events.…”

Motion Flow-Based Video Retrieval

“…A large number of motion-based video retrieval systems have been proposed in the past decade [1], [2], [5], [6], [11]- [13]. VideoQ [2] is a notable approach that directly addresses motion-based characterization of video content.…”

“…The constituents of their video database comprise a set of trajectories formed by linking the centroids of video objects across consecutive frames. In [5], Dagtas et al proposed using a combination of trajectory-and trail-based models to characterize the motion of a video object. They adopted a Fourier transform-based similarity metric and a two-stage comparison strategy to search for similar trajectories.…”