Scene extraction in motion pictures

This paper addresses the challenge of bridging the semantic gap between the rich meaning users desire when they query to locate and browse media and the shallowness of media descriptions that can be computed in today\u27s content management systems. To facilitate high-level semantics-based content annotation and interpretation, we tackle the problem of automatic decomposition of motion pictures into meaningful story units, namely scenes. Since a scene is a complicated and subjective concept, we first propose guidelines from fill production to determine when a scene change occurs. We then investigate different rules and conventions followed as part of Fill Grammar that would guide and shape an algorithmic solution for determining a scene. Two different techniques using intershot analysis are proposed as solutions in this paper. In addition, we present different refinement mechanisms, such as film-punctuation detection founded on Film Grammar, to further improve the results. These refinement techniques demonstrate significant improvements in overall performance. Furthermore, we analyze errors in the context of film-production techniques, which offer useful insights into the limitations of our method

Developmental changes in the engagement of episodic retrieval processes and their relationship with working memory during the period of middle childhood.

We examined the development of children’s engagement of the episodic retrieval processes of recollection and familiarity and their relationship with working memory (WM). Ninety-six children (24 in four groups aged 8, 9, 10, and 11 years) and 24 adults performed an episodic memory (EM) task involving old/new, remember/know (R/K), and source memory judgements and numerous WM tasks that assessed verbal and spatial components of WM and delayed short-term memory (STM). Developmental changes were observed in EM with younger children (8-, 9-, 10-year-olds) making fewer remember responses than 11-year-olds and adults while 11-year-olds did not differ from adults. Only children aged 10 years plus showed a relationship between EM and WM. EM was related to verbal executive WM in 10- and 11-year-old children suggesting that children at this stage use verbal strategies to aid EM. In contrast, EM was related to spatial executive WM in adults. The engagement of episodic retrieval processes appears to be selectively related to executive components of verbal and spatial WM, the pattern of which differs in children and adults

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Two-dimensional string notation for representing video sequences

Most current work on video indexing concentrates on queries which operate over high level semantic information which must be entirely composed and entered manually. We propose an indexing system which is based on spatial information about key objects in a scene. These key objects may be detected automatically, with manual supervision, and tracked through a sequence using one of a number of recently developed techniques. This representation is highly compact and allows rapid resolution of queries specified by iconic example. A number of systems have been produced which use 2D string notations to index digital image libraries. Just as 2D strings provide a compact and tractable indexing notation for digital pictures, a sequence of 2D strings might provide an index for a video or image sequence. To improve further upon this we reduce the representation to the 2D string pair representing the initial frame, and a sequence of edits to these strings. This takes advantage of the continuity between frames to further reduce the size of the notation. By representing video sequences using string edits, a notation has been developed which is compact, and allows querying on the spatial relationships of objects to be performed without rebuilding the majority of the scene. Calculating ranks of objects directly from the edit sequence allows matching with minimal calculation, thus greatly reducing search time. This paper presents the edit sequence notation and algorithms for evaluating queries over image sequences. A number of optimizations which represent a considerably saving in search time is demonstrated in the paper

Shape-based Image Retrieval Using Spatio-temporal Relation Computations

[[abstract]]Content-based retrieval of multimedia information is one of the most difficult research topics in multimedia computing and information retrieval. In this paper, we present a visual system which allows content-based retrieval of still image. The recognition algorithms we used are based on spatio-temporal relations. Two approaches and algorithms were developed based on the similarity between polygons. The system is incorporated with a visual interface which allows the user to specify polygons as the shape specification of pictures. The preliminary experience shows that, within an image database containing about 300 bitmapped images, the system is able to retrieve correct information of a high satisfaction.[[notice]]補正完

Serial Position Effects in Short-term Visual Memory: A SIMPLE Explanation?

A version of Sternberg’s (1966) short-term, visual memory recognition paradigm with pictures of unfamiliar faces as stimuli was used in three experiments to assess the applicability of the distinctiveness based SIMPLE model proposed by Brown, Neath & Chater (2002). Initial simulations indicated that the amount of recency predicted increased as the parameter measuring the psychological distinctiveness of the stimulus material (c) increased, and that the amount of primacy was dependent on the extent of proactive interference from previously presented stimuli. The data from experiment 1, which used memory lists of four and five faces varying in visual similarity confirmed the predicted, extended recency effect. However, changes in visual similarity were not found to produce changes in c. In Experiments 2 and 3, the conditions that influence the magnitude of c were explored. These revealed that both the familiarity of the stimulus class before testing, and changes in familiarity due to perceptual learning, influenced distinctiveness as indexed by the parameter c. Overall the empirical data from all three experiments were well-fit by SIMPLE

Content-based Image Retrieval by Spatial Similarity

Similarity-based retrieval of images is an important task in image databases. Most of the user's queries are on retrieving those database images that are spatially similar to a query image. In defence strategies, one wants to know a number of armoured vehicles, such as battle tanks, portable missile launching vehicles, etc. moving towards it, so that one can decide counter strategy. Content-based spatial similarity retrieval of images can be used to locate spatial relationship of various objects in a specific area from the aerial photographs and to retrieve images similar to the query image from image database. A content-based image retrieval system that efficiently and effectively retrieves information from a defence image database along with the architecture for retrieving images by spatial similarity is presented. A robust algorithm SIMdef for retrieval by spatial similarity is proposed that utilises both directional and topological relations for computing similarity between images, retrieves similar images and recognises images even after they undergo modelling transformations (translation, scale and rotation). A case study for some of the common objects, used in defence applications using SIMdef algorithm, has been done

Al Hybrid Content-Based Retrieval Approach For Video Data

Increasing use of multimedia data makes it crucial to develop intelligent search mec:hanisms for retrieving multimedia data by content. Traditional text-based methods clearly do not suffice to describe the rich content of images, voice or video. Digital vidseo requires the incorporation of temporal information for any effective contentbased retrieval scheme. We present a novel technique which integrates object motion ancl temporal relationship information in order to characterize the events for subsequent search for similar clips. We propose a hybrid mechanism based on object motion trails similarity match and interval-based temporal modeling that leads to a unique framework for spatio-temporal content based access in digital video. We implemented the proposed methods and demonstrated that high-level query formulation can be achieved for the aforementioned purpose. Development of such technology will enable true multimedia search engines that will accomplish what current Internet search engines like Infoseek or Excite do today for textual data