Multimedia Information Filtering

Multimedia Information Filtering

Minaz J. Parmar (Brunel University, UK) and Marios C. Angelides (Brunel University, UK)
DOI: 10.4018/978-1-60566-026-4.ch439
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Abstract

In the film Minority Report (20th Century Fox, 2002), which is set in the near future, there is a scene where a man walks into a department store and is confronted by a holographic shop assistant. The holographic shop assistant recognises the potential customer by iris-recognition technology. The holographic assistant then welcomes the man by his name and starts to inform him of offers and items that he would be interested in based on his past purchases and what other shoppers who have similar tastes have purchased. This example of future personalised shopping assistants that can help a customer find shopping goods is not too far away from becoming reality in some form or another. Malone, Grant, Turbak, Brobst, and Cohen (1987) introduced three paradigms for information selection, cognitive, economic, and social, based on their work with a system they called the Information Lens. Their definition of cognitive filtering, the approach actually implemented by the Information Lens, is equivalent to the “content filter” defined earlier by Denning, and this approach is now commonly referred to as “content-based” filtering. Their most important contribution was to introduce an alternative approach that they called social (now also more commonly called collaborative) filtering. In social filtering, the representation of a document is based on annotations to that document made by prior readers of the document. In the 1990s much work was done on collaborative filtering (CF). There were three systems that were considered to be the quintessential recommender systems. The Grouplens project (Miller, Albert, Lam, Konstan, & Riedl, 2003) initially was used for filtering items from the Usenet news domain. This later became the basis of Movielens. The Bellcore Video recommender system (Hill, Stead, Rosenstein, & Furnas, 1995), which recommended video films to users based on what they had rented before, and Ringo (Shardanand & Maes, 1995), which later was published on the Web and marketed as Firefly, used social filtering to recommend movies and music.
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Introduction

In the film Minority Report (20th Century Fox, 2002), which is set in the near future, there is a scene where a man walks into a department store and is confronted by a holographic shop assistant. The holographic shop assistant recognises the potential customer by iris-recognition technology. The holographic assistant then welcomes the man by his name and starts to inform him of offers and items that he would be interested in based on his past purchases and what other shoppers who have similar tastes have purchased. This example of future personalised shopping assistants that can help a customer find shopping goods is not too far away from becoming reality in some form or another.

Malone, Grant, Turbak, Brobst, and Cohen (1987) introduced three paradigms for information selection, cognitive, economic, and social, based on their work with a system they called the Information Lens. Their definition of cognitive filtering, the approach actually implemented by the Information Lens, is equivalent to the “content filter” defined earlier by Denning, and this approach is now commonly referred to as “content-based” filtering. Their most important contribution was to introduce an alternative approach that they called social (now also more commonly called collaborative) filtering. In social filtering, the representation of a document is based on annotations to that document made by prior readers of the document.

In the 1990s much work was done on collaborative filtering (CF). There were three systems that were considered to be the quintessential recommender systems. The Grouplens project (Miller, Albert, Lam, Konstan, & Riedl, 2003) initially was used for filtering items from the Usenet news domain. This later became the basis of Movielens. The Bellcore Video recommender system (Hill, Stead, Rosenstein, & Furnas, 1995), which recommended video films to users based on what they had rented before, and Ringo (Shardanand & Maes, 1995), which later was published on the Web and marketed as Firefly, used social filtering to recommend movies and music.

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Background

Filtering multimedia content is an extensive process that involves extracting and modeling semantic and structural information about the content as well as metadata (Angelides, 2003). The problem with multimedia content is that the information presented in any document is multimodal by definition. Attributes of different types of media vary considerably in the way the format of the content is stored and perceived. There is no direct way of correlating the semantic content of a video stream with that of an audio stream unless it is done manually. A content model of the spatial and temporal characteristics of the objects can be used to define the actions the objects take part in. This content model can then be filtered against a user profile to allow granular filtering of the content, allowing for effective ranking and relevancy of the documents.

Filtering has mainly been investigated in the domain of text documents. The user’s preferences are used as keywords, which are used by the filters as criteria for separating the textual documents into relevant and irrelevant content. The more positive keywords contained in a document, the more relevant the document becomes. Techniques such as latent semantic indexing have found ways of interpreting the meaning of a word in different contexts to allow accurate filtering of documents using different syntax, but allow the same semantics to be recognised and understood.

Text documents adhere to the standards of the language they are written in. Trying to do the same for AV data streams, you are faced with the problem of identifying the terms in the content itself. The terms are represented as a series of objects that appear in the content, for example, a face in an image file. These terms cannot be directly related to the objects as there is no method of comparison, or if there is, it is complex to unlock. The title of the document and some information might be provided in the file description, but the actions and spatial and temporal characteristics of the objects will not be described to a sufficient level for effective analysis of relevancy. (see Table 1)

Key Terms in this Chapter

Recommender systems: Assist and augment the transfer of recommendations between members of a community

User Profile: A data log representing a model of a user that can be used to ascertain behaviour and taste preferences

Recommendation: A filtered list of alternatives (items of interest) that support a decision-making process

Content-Based Filtering: Organizes information based on properties of the object of preference and/or the carrier of information

Collaborative Filtering: Aims at exploiting preference behaviour and qualities of other persons in speculating about the preferences of a particular individual

Hybrid Filtering: A combination of filtering techniques in which the disadvantages of one type of filtering is counteracted by the advantages of another

Information Filtering: Filtering information from a dynamic information space based on a user’s long-term information needs

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