Simple Tools for Abstraction and Instantiation

A simplified version of tools for abstraction and instantiation [1,2,3] are proposed here. The tools are merely two new attributes applicable to any DS instances. The merit of abstraction and instantiation is the economy of description and clarification of structural commonality. Abstractions allow you to avoid repeating similar descriptions. You write one template to address the shared structure of descriptions and reuse it when you want similar descriptions. Suppose you want to describe many occurrences of a common pattern of events and states of affairs, such as a type of configuration of soccer players in the field. You will describe the positions of eleven or twenty two people to address this configuration. Once you have described this common pattern, you can capture each occurrence of the pattern by just substituting the eleven or twenty-two people to with particular players, without repeating the descriptions of their positions, among others. Although the AbstractionLevel DataType in MPEG-7 FDIS is apparently claimed to address abstraction and instantiation, its usage and semantics have not been clarified enough to actually employ it. What follows should provide a far simpler and practically usable set of tools for abstraction and instantiation

Report of CE on Abstraction and Instantiation

The CE on Abstraction and Instantiation [1] is reported below according to the workplan proposed at the Singapore meeting [2]

Report of CE on Semantic DS

The Semantic DS allows describing the world depicted by the AV content and interpreting that world, i.e., the “about” of the AV content or depicted narrative reality, which sometimes is imaginary. This document reports on the core experiment on the Semantic DS [6]. The CE originally started at the Maui meeting in December 1999 [3]. Progress reports of the CE were provided at the Geneva meeting [1] and at the Beijing meeting [2]. In Beijing, some components of the Semantic DS were promoted to the XM: Semantic DS, Object DS, PersonObject DS, Event DS, State DS, MediaOccurrence DS, SemanticTime DS, SemanticLocation DS, UsageLabel D, and some semantic relations. The main tasks of this core experiment have been the following: 1) To refine the specification of the Semantic DS by solving identified issues; 2) To define the Conceptual DSs; 3) To recommend the standardization of more semantic relations; 4) To investigate the use of membership functions to describe the strength of relations; 5) To generate simple and complex semantic descriptions of multimedia material; 6) To implement a retrieval and browsing application/s that uses the generated descriptions and that shows the functionality of the UsageLabel D, the Conceptual DSs, the State DS, and membership functions for relations, especially; 7) To recommend changes and additions to the Semantic DS based on the results of the experiment. The retrieval application that the CE has accomplished two objectives: (1) to show the utility of the components of the Semantic DS in a retrieval scenario, and (2) to be the software for the MPEG-7 XM platform

Report of CE on Semantic DS

This document reports on the core experiment on the Semantic DS [5]. The Semantic DS allows describing the world depicted by the AV content and interpreting that world, i.e., the “about” of the AV content or depicted narrative reality, which sometimes is imaginary. The CE originally started at the Maui meeting in December 1999 [4]. Progress reports of the CE were provided at the Geneva meeting [1], at the Beijing meeting [3], and at the La Baule meeting [2]. In La Baule, some components of the Semantic DS were promoted to the WD - Semantic DS, SemanticBase DS, Object DS, Event DS, AgentObject DS, SemanticPlace DS, SemanticTime DS, MediaOccurrence DS, and semantic relations-, and others were promoted to the XM – SemanticState DS, Concept DS, and AbstractionLevel datatype. The goal of this CE is to continue the refinement and evaluation of the Semantic DS and to continue the evaluation of the use of membership functions to describe relation strength. The AbstractionLevel datatype was promoted to CD before Pisa. At Pisa, the work to update the specification of the the SemanticTime, SemanticPlace, and Event DSs and to explain the methods for abstraction and the use of abstract concepts was started. The main tasks of this core experiment have been the following: 1. To refine the specification of the Semantic DS by solving open issues identified by reviewers and previous CEs; 2. To recommend the standardization of more semantic relations; 3. To continue the investigation of the use of membership functions to describe the strength of relations; 4. To generate simple and complex semantic descriptions of multimedia material, 5. To continue the implementation of a retrieval and browsing application/s that use/s the generated descriptions and that show/s the functionality of the DSs in the MDS XM, and 6. To recommend changes and additions to the Semantic DS based on the results of the experiment. The retrieval application that the CE continued the development of the software that had already been integrated into the XM for the Semantic DS

Report of CE on Semantic DS

ISO/IEC JTC1/SC29/WG11, MPEG00/M6355, 53rd meeting, Jul. 2000, Beijing, PR

Semantics of Multimedia in MPEG-7

In this paper, we present the tools standardized by MPEG-7 for describing the semantics of multimedia. In particular, we focus on the abstraction model, entities, attributes and relations of MPEG-7 semantic descriptions. MPEG-7 tools can describe the semantics of specific instances of multimedia such as one image or one video segment but can also generalize these descriptions either to multiple instances of multimedia or to a set of semantic descriptions. The key components of MPEG-7 semantic descriptions are semantic entities such as objects and events, attributes of these entities such as labels and properties, and, finally, relations of these entities such as an object being the patient of an event. The descriptive power and usability of these tools has been demonstrated in numerous experiments and applications, these make them key candidates to enable intelligent applications that deal with multimedia at human levels

Discovering the Language of Wine Reviews: A Text Mining Account

It is widely held that smells and flavors are impossible to put into words. In this paper we test this claim by seeking predictive patterns in wine reviews, which ostensibly aim to provide guides to perceptual content. Wine reviews have previously been critiqued as random and meaningless. We collected an English corpus of wine reviews with their structured metadata, and applied machine learning techniques to automatically predict the winetextquotesingles color, grape variety, and country of origin. To train the three supervised classifiers, three different information sources were incorporated: lexical bag-of-words features, domain-specific terminology features, and semantic word embedding features. In addition, using regression analysis we investigated basic review properties, i.e., review length, average word length, and their relationship to the scalar values of price and review score. Our results show that wine experts do share a common vocabulary to describe wines and they use this in a consistent way, which makes it possible to automatically predict wine characteristics based on the review text alone. This means that odors and flavors may be more expressible in language than typically acknowledged