Personalized Content-Based Image Retrieval

Personalized Content-Based Image Retrieval

Iker Gondra (St. Francis Xavier University, Canada)
DOI: 10.4018/978-1-60566-174-2.ch012
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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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In recent years, the rapid development of information technologies and the advent of the Web have accelerated the growth of digital media and, in particular, image collections. As a result and in order to realize the full potential of these technologies, the need for effective mechanisms to search large image collections becomes evident. The management of text information has been studied thoroughly and there have been many successful approaches for handling text databases (see (Salton, 1986)). However, the progress in research and development of multimedia database systems has been slow due to the difficulties and challenges of the problem.

The development of concise representations of images that can capture the essence of their visual content is an important task. However, as the saying “A picture is worth a thousand words” suggests, representing visual content is a very difficult task. The human ability to extract semantics from an image by using knowledge of the world is remarkable, though probably difficult to emulate.

At present, the most common way to represent the visual content of an image is to assign a set of descriptive keywords to it. Then, image retrieval is performed by matching the query text with the stored keywords (Rui, 1998). However, there are many problems associated with this simple keyword matching approach. First, it is usually the case that all the information contained in an image cannot be captured by a few keywords. Furthermore, a large amount of effort is needed to do keyword assignments in a large image database. Also, because different people may have different interpretations of an image's content, there will be inconsistencies (Rui, 1998). Consider the image in Figure 1. One might describe it as “mountains”, “trees”, and “lake”. However, that particular description would not be able to respond to user queries for “water”, “landscape”, “peaceful”, or “water reflection”.

Figure 1.

Sample image

In order to alleviate some of the problems associated with text-based approaches, content-based image retrieval (CBIR) was proposed (see (Faloutsos, 1993) for examples of early approaches). The idea is to search on the images directly. A set of low-level features (such as color, texture, and shape) are extracted from the image to characterize its visual content. In traditional approaches (Faloutsos, 1993; Gupta, 1997; Hara, 1997; Kelly, 1995; Mehrotra, 1997; Pentland, 1994; Samadani, 1993; Sclaroff, 1997; Smith, 1996; Smith, 1997; Stone, 1996; Wang, 1998), each image is represented by a set of global features that are calculated by means of uniform processing over the entire image and describe its visual content (e.g., color, texture). The features are then the components of a feature vector which makes the image correspond to a point in a feature space.

Complete Chapter List

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Table of Contents
Zongmin Ma
Chapter 1
Danilo Avola, Fernando Ferri, Patrizia Grifoni
The novel technologies used in different application domains allow obtaining digital images with a high complex informative content, which can be... Sample PDF
Genetic Algorithms and Other Approaches in Image Feature Extraction and Representation
Chapter 2
Dany Gebara, Reda Alhajj
This chapter presents a novel approach for content-fbased image retrieval and demonstrates its applicability on non-texture images. The process... Sample PDF
Improving Image Retrieval by Clustering
Chapter 3
Gang Zhang, Z. M. Ma, Li Yan
Texture feature extraction and description is one of the important research contents in content-based medical image retrieval. The chapter first... Sample PDF
Review on Texture Feature Extraction and Description Methods in Content-Based Medical Image Retrieval
Chapter 4
Jafar M. Ali
Advances in data storage and image acquisition technologies have enabled the creation of large image datasets. Thus, it is necessary to develop... Sample PDF
Content-Based Image Classification and Retrieval: A Rule-Based System Using Rough Sets Framework
Chapter 5
David García Pérez, Antonio Mosquera, Stefano Berretti, Alberto Del Bimbo
Content-based image retrieval has been an active research area in past years. Many different solutions have been proposed to improve performance of... Sample PDF
Content Based Image Retrieval Using Active-Nets
Chapter 6
Ming Zhang, Reda Alhajj
Content-Based Image Retrieval (CBIR) aims to search images that are perceptually similar to the querybased on visual content of the images without... Sample PDF
Content-Based Image Retrieval: From the Object Detection/Recognition Point of View
Chapter 7
Chotirat “Ann” Ratanamahatana, Eamonn Keogh, Vit Niennattrakul
After the generation of multimedia data turning digital, an explosion of interest in their data storage, retrieval, and processing, has drastically... Sample PDF
Making Image Retrieval and Classification More Accurate Using Time Series and Learned Constraints
Chapter 8
Hakim Hacid, Abdelkader Djamel Zighed
A multimedia index makes it possible to group data according to similarity criteria. Traditional index structures are based on trees and use the... Sample PDF
A Machine Learning-Based Model for Content-Based Image Retrieval
Chapter 9
Ruofei Zhang, Zhongfei (Mark) Zhang
This chapter studies the user relevance feedback in image retrieval. We take this problem as a standard two-class pattern classification problem... Sample PDF
Solving the Small and Asymmetric Sampling Problem in the Context of Image Retrieval
Chapter 10
Chia-Hung Wei, Chang-Tsun Li
An image is a symbolic representation; people interpret an image and associate semantics with it based on their subjective perceptions, which... Sample PDF
Content Analysis from User's Relevance Feedback for Content-Based Image Retrieval
Chapter 11
Pawel Rotter, Andrzej M.J. Skulimowski
In this chapter, we describe two new approaches to content-based image retrieval (CBIR) based on preference information provided by the user... Sample PDF
Preference Extraction in Image Retrieval
Chapter 12
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 13
Zhiping Shi, Qingyong Li, Qing He, Zhongzhi Shi
Semantics-based retrieval is a trend of the Content-Based Multimedia Retrieval (CBMR). Typically, in multimedia databases, there exist two kinds of... Sample PDF
A Semantics Sensitive Framework of Organization and Retrieval for Multimedia Databases
Chapter 14
Chia-Hung Wei, Chang-Tsun Li, Yue Li
As distributed mammogram databases at hospitals and breast screening centers are connected together through PACS, a mammogram retrieval system is... Sample PDF
Content-Based Retrieval for Mammograms
Chapter 15
Ying-li Tian, Arun Hampapur, Lisa Brown, Rogerio Feris, Max Lu, Andrew Senior
Video surveillance automation is used in two key modes: watching for known threats in real-time and searching for events of interest after the fact.... Sample PDF
Event Detection, Query, and Retrieval for Video Surveillance
Chapter 16
Min Chen, Shu-Ching Chen
This chapter introduces an advanced content-based image retrieval (CBIR) system, MMIR, where Markov model mediator (MMM) and multiple instance... Sample PDF
MMIR: An Advanced Content-Based Image Retrieval System Using a Hierarchical Learning Framework
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