MMIR: An Advanced Content-Based Image Retrieval System Using a Hierarchical Learning Framework

MMIR: An Advanced Content-Based Image Retrieval System Using a Hierarchical Learning Framework

Min Chen (Florida International University, USA) and Shu-Ching Chen (Florida International University, USA)
DOI: 10.4018/978-1-60566-174-2.ch016
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Abstract

This chapter introduces an advanced content-based image retrieval (CBIR) system, MMIR, where Markov model mediator (MMM) and multiple instance learning (MIL) techniques are integrated seamlessly and act coherently as a hierarchical learning engine to boost both the retrieval accuracy and efficiency. It is well-understood that the major bottleneck of CBIR systems is the large semantic gap between the low-level image features and the high-level semantic concepts. In addition, the perception subjectivity problem also challenges a CBIR system. To address these issues and challenges, the proposed MMIR system utilizes the MMM mechanism to direct the focus on the image level analysis together with the MIL technique (with the neural network technique as its core) to real-time capture and learn the object-level semantic concepts with some help of the user feedbacks. In addition, from a long-term learning perspective, the user feedback logs are explored by MMM to speed up the learning process and to increase the retrieval accuracy for a query. The comparative studies on a large set of real-world images demonstrate the promising performance of our proposed MMIR system.
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Introduction

Content-based image retrieval (CBIR), which was proposed in the early 1990s, has attracted a broad range of research interests from many computer communities in the past decade. Generally speaking, in a CBIR system, each image is first mapped to a point in a certain feature space, where the features can be categorized into color (Stehling, Nascimento, & Falcao, 2000), texture (Kaplan et al., 1998), shape (Zhang & Lu, 2002), and so forth. Next, given a query in terms of image examples, the system retrieves images with regard to their features (He, Li, Zhang, Tong, & Zhang, 2004). Though extensive research efforts have been directed into this area, it still remains a big challenge and an open issue in terms of retrieving the desired images from the large image repositories effectively and efficiently. In short, some of the major obstacles can be summarized as follows.

  • First, it is widely accepted that the major bottleneck of CBIR systems is the large semantic gap between the low-level image features and high-level semantic concepts, which prevents the systems from being applied to real applications (Hoi & Lyu, 2004).

  • Second, the perception subjectivity problem poses additional challenges for CBIR systems. In other words, in viewing the same image (e.g., Figure 1a), different users might possess various interests in either a certain object (e.g., the house, the tree, etc.) or the entire image (e.g., a landscape during the autumn season). In this case, Figure 1b, Figure 1c, or Figure 1d, respectively, might be considered as the relevant image with regard to Figure 1a. In addition, even a same user can have different perceptions toward the same image at various situations and with different purposes.

    Figure 1.

    Example images

To address the earlier-mentioned challenges and issues, a certain form of adaptive (i.e., data-driven) description is required to capture the salient meaning of each image. In addition, the system should be able to expedite the navigation process through a large image database with the facilitation of users’ relevance feedbacks. In other words, the search engine should be equipped with an inference engine to observe and learn from user interactions. To this extent, we believe that there are both a need and an opportunity to systematically incorporate machine learning techniques into an integrated approach for content-based image retrieval. In this chapter, we introduce an advanced content-based image retrieval system called MMIR, where Markov model mediator (MMM) and multiple instance learning (MIL) techniques are integrated seamlessly and act coherently as a hierarchical learning engine to boost both the retrieval accuracy and efficiency.

Markov model mediator (MMM) is a statistical reasoning mechanism, which adopts the mathematically sound Markov model and the concept of mediators. As presented in our earlier studies (Shyu, Chen, Chen, Zhang, & Shu, 2003; Shyu, Chen, & Rubin, 2004a), MMM possesses the extraordinary capability in exploring the semantic concepts in the image level from the long-term learning perspective. In contrast, multiple instance learning (MIL) incorporated with the neural network (NN) technique aims at learning the region of interests based on the users’ relevance feedbacks on the whole image in real time. Integrating the essential functionalities from both MMM and MIL has the potential in constructing a robust CBIR system, which is the attempt of this study.

The remainder of this chapter is organized as follows. The next section, Background and Related Work, gives a broad background introduction as well as the literature review. The system is detailed in the Hierarchical Learning Scheme section and the Experimental Results section, followed by the discussions of the possible future trends in terms of the CBIR research in the Future Trends section. Finally, the chapter ends with the Conclusions section.

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Table of Contents
Acknowledgment
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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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About the Contributors