Personalized Content-Based Image Retrieval

Personalized Content-Based Image Retrieval

Iker Gondra (St. Francis Xavier University, Canada)
DOI: 10.4018/978-1-59904-510-8.ch009
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In content-based image retrieval (CBIR), a set of low-level features are extracted from an image to represent its visual content. Retrieval is performed by image example where a query image is given as input by the user and an appropriate similarity measure is used to find the best matches in the corresponding feature space. This approach suffers from the fact that there is a large discrepancy between the low-level visual features that one can extract from an image and the semantic interpretation of the image’s content that a particular user may have in a given situation. That is, users seek semantic similarity, but we can only provide similarity based on low-level visual features extracted from the raw pixel data, a situation known as the semantic gap. The selection of an appropriate similarity measure is thus an important problem. Since visual content can be represented by different attributes, the combination and importance of each set of features varies according to the user’s semantic intent. Thus, the retrieval strategy should be adaptive so that it can accommodate the preferences of different users. Relevance feedback (RF) learning has been proposed as a technique aimed at reducing the semantic gap. It works by gathering semantic information from user interaction. Based on the user’s feedback on the retrieval results, the retrieval scheme is adjusted. By providing an image similarity measure under human perception, RF learning can be seen as a form of supervised learning that finds relations between high-level semantic interpretations and low-level visual properties. That is, the feedback obtained within a single query session is used to personalize the retrieval strategy and thus enhance retrieval performance. In this chapter we present an overview of CBIR and related work on RF learning. We also present our own previous work on a RF learning-based probabilistic region relevance learning algorithm for automatically estimating the importance of each region in an image based on the user’s semantic intent.

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Table of Contents
Rafael Andrés González, Nong Chen, Ajantha Dahanayake
Chapter 1
Shan Chen, Mary-Anne Williams
Ontology learning has been identified as an inherently transdisciplinary area. Personalized ontology learning for Web personalization involves Web... Sample PDF
Learning Personalized Ontologies from Text: A Review on an Inherently Transdisciplinary Area
Chapter 2
Nikos Manouselis, Constantina Costopoulou
The problem of collaborative filtering is to predict how well a user will like an item that he or she has not rated, given a set of historical... Sample PDF
Overview of Design Options for Neighborhood-Based Collaborative Filtering Systems
Chapter 3
Rafael A. Gonzalez
In this chapter, information management problems and some of the computer-based solutions offered to deal with them are presented. The claim is that... Sample PDF
Exploring Information Management Problems in the Domain of Critical Incidents
Chapter 4
Penelope Markellou, Maria Rigou, Spiros Sirmakessis
The Web has become a huge repository of information and keeps growing exponentially under no editorial control, while the human capability to find... Sample PDF
Mining for Web Personalization
Chapter 5
Athena Vakali, Geroge Pallis, Lefteris Angelis
The explosive growth of the Web scale has drastically increased information circulation and dissemination rates. As the number of both Web users and... Sample PDF
Clustering Web Information Sources
Chapter 6
Nong Chen, Ajantha Dahanayake
Personalized information seeking and retrieval is regarded as the solution to the problem of information overload in domains such as crisis response... Sample PDF
A Conceptual Structure for Designing Personalized Information Seeking and Retrieval Systems in Data-Intensive Domains
Chapter 7
Amr Ali Eldin, Zoran Stojanovic
With the rapid developments of mobile telecommunications technology over the last two decades, a new computing paradigm known as ‘anywhere and... Sample PDF
Privacy Control Requirements for Context-Aware Mobile Services
Chapter 8
Ricardo Barros, Geraldo Xexéo, Wallace A. Pinheiro, Jano de Souza
Due to the amount of information on the Web being so large and being of varying levels of quality, it is becoming increasingly difficult to find... Sample PDF
User and Context-Aware Quality Filters Based on Web Metadata Retrieval
Chapter 9
Iker Gondra
In content-based image retrieval (CBIR), a set of low-level features are extracted from an image to represent its visual content. Retrieval is... Sample PDF
Personalized Content-Based Image Retrieval
Chapter 10
Lu Yan
Humans are quite successful at conveying ideas to each other and retrieving information from interactions appropriately. This is due to many... Sample PDF
Service-Oriented Architectures for Context-Aware Information Retrieval and Access
Chapter 11
Zakaria Maamar, Soraya Kouadri Mostéfaoui, Qusay H. Mahmoud
This chapter presents a context-based approach for Web services personalization so that user preferences are accommodated. Preferences are of... Sample PDF
On Personalizing Web Services Using Context
Chapter 12
Haibin Zhu, MengChu Zhou
Agent system design is a complex task challenging designers to simulate intelligent collaborative behavior. Roles can reduce the complexity of agent... Sample PDF
Role-Based Multi-Agent Systems
Chapter 13
Tarek Ben Mena, Narjès Bellamine-Ben Saoud, Mohamed Ben Ahmed, Bernard Pavard
This chapter aims to define context notion for multi-agent systems (MAS). Starting from the state of the art on context in different disciplines, we... Sample PDF
Towards a Context Definition for Multi-Agent Systems
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