Intelligent Image Retrieval Techniques: A Survey

AbstractIn the current era of digital communication, the use of digital images has increased for expressing, sharing and interpreting information. While working with digital images, quite often it is necessary to search for a specific image for a particular situation based on the visual contents of the image. This task looks easy if you are dealing with tens of images but it gets more difficult when the number of images goes from tens to hundreds and thousands, and the same content-based searching task becomes extremely complex when the number of images is in the millions. To deal with the situation, some intelligent way of content-based searching is required to fulfill the searching request with right visual contents in a reasonable amount of time. There are some really smart techniques proposed by researchers for efficient and robust content-based image retrieval. In this research, the aim is to highlight the efforts of researchers who conducted some brilliant work and to provide a proof of concept for intelligent content-based image retrieval techniques

Effective Graph-Based Content--Based Image Retrieval Systems for Large-Scale and Small-Scale Image Databases

This dissertation proposes two novel manifold graph-based ranking systems for Content-Based Image Retrieval (CBIR). The two proposed systems exploit the synergism between relevance feedback-based transductive short-term learning and semantic feature-based long-term learning to improve retrieval performance. Proposed systems first apply the active learning mechanism to construct users\u27 relevance feedback log and extract high-level semantic features for each image. These systems then create manifold graphs by incorporating both the low-level visual similarity and the high-level semantic similarity to achieve more meaningful structures for the image space. Finally, asymmetric relevance vectors are created to propagate relevance scores of labeled images to unlabeled images via manifold graphs. The extensive experimental results demonstrate two proposed systems outperform the other state-of-the-art CBIR systems in the context of both correct and erroneous users\u27 feedback

Bridging the semantic gap in content-based image retrieval.

To manage large image databases, Content-Based Image Retrieval (CBIR) emerged as a new research subject. CBIR involves the development of automated methods to use visual features in searching and retrieving. Unfortunately, the performance of most CBIR systems is inherently constrained by the low-level visual features because they cannot adequately express the user\u27s high-level concepts. This is known as the semantic gap problem. This dissertation introduces a new approach to CBIR that attempts to bridge the semantic gap. Our approach includes four components. The first one learns a multi-modal thesaurus that associates low-level visual profiles with high-level keywords. This is accomplished through image segmentation, feature extraction, and clustering of image regions. The second component uses the thesaurus to annotate images in an unsupervised way. This is accomplished through fuzzy membership functions to label new regions based on their proximity to the profiles in the thesaurus. The third component consists of an efficient and effective method for fusing the retrieval results from the multi-modal features. Our method is based on learning and adapting fuzzy membership functions to the distribution of the features\u27 distances and assigning a degree of worthiness to each feature. The fourth component provides the user with the option to perform hybrid querying and query expansion. This allows the enrichment of a visual query with textual data extracted from the automatically labeled images in the database. The four components are integrated into a complete CBIR system that can run in three different and complementary modes. The first mode allows the user to query using an example image. The second mode allows the user to specify positive and/or negative sample regions that should or should not be included in the retrieved images. The third mode uses a Graphical Text Interface to allow the user to browse the database interactively using a combination of low-level features and high-level concepts. The proposed system and ail of its components and modes are implemented and validated using a large data collection for accuracy, performance, and improvement over traditional CBIR techniques

Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making

Machine learning (ML) is increasingly being used in image retrieval systems for medical decision making. One application of ML is to retrieve visually similar medical images from past patients (e.g. tissue from biopsies) to reference when making a medical decision with a new patient. However, no algorithm can perfectly capture an expert's ideal notion of similarity for every case: an image that is algorithmically determined to be similar may not be medically relevant to a doctor's specific diagnostic needs. In this paper, we identified the needs of pathologists when searching for similar images retrieved using a deep learning algorithm, and developed tools that empower users to cope with the search algorithm on-the-fly, communicating what types of similarity are most important at different moments in time. In two evaluations with pathologists, we found that these refinement tools increased the diagnostic utility of images found and increased user trust in the algorithm. The tools were preferred over a traditional interface, without a loss in diagnostic accuracy. We also observed that users adopted new strategies when using refinement tools, re-purposing them to test and understand the underlying algorithm and to disambiguate ML errors from their own errors. Taken together, these findings inform future human-ML collaborative systems for expert decision-making

A Radial Basis Function and Semantic Learning Space Based Composite Learning Approach to Image Retrieval

This paper introduces a composite learning approach for image retrieval with relevance feedback. The proposed system combines the radial basis function (RBF) based low-level learning and the semantic learning space (SLS) based high-level learning to retrieve the desired images with fewer than 3 feedback steps. User’s relevance feedback is utilized for updating both low-level and high-level features of the query image. Specifically, the RBF-based learning captures the non-linear relationship between the low-level features and the semantic meaning of an image. The SLS-based learning stores semantic features of each database image using randomly chosen semantic basis images. The similarity score is computed as the weighted combination of normalized similarity scores yielded from both RBF and SLS learning. Extensive experiments evaluate the performance of the proposed approach and demonstrate our system achieves higher retrieval accuracy than peer systems. Index Terms — Radial basis function, semanti

Content-based image retrieval: reading one's mind and helping people share.

Sia Ka Cheung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 85-91).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Problem Statement --- p.1Chapter 1.2 --- Contributions --- p.3Chapter 1.3 --- Thesis Organization --- p.4Chapter 2 --- Background --- p.5Chapter 2.1 --- Content-Based Image Retrieval --- p.5Chapter 2.1.1 --- Feature Extraction --- p.6Chapter 2.1.2 --- Indexing and Retrieval --- p.7Chapter 2.2 --- Relevance Feedback --- p.7Chapter 2.2.1 --- Weight Updating --- p.9Chapter 2.2.2 --- Bayesian Formulation --- p.11Chapter 2.2.3 --- Statistical Approaches --- p.12Chapter 2.2.4 --- Inter-query Feedback --- p.12Chapter 2.3 --- Peer-to-Peer Information Retrieval --- p.14Chapter 2.3.1 --- Distributed Hash Table Techniques --- p.16Chapter 2.3.2 --- Routing Indices and Shortcuts --- p.17Chapter 2.3.3 --- Content-Based Retrieval in P2P Systems --- p.18Chapter 3 --- Parameter Estimation-Based Relevance Feedback --- p.21Chapter 3.1 --- Parameter Estimation of Target Distribution --- p.21Chapter 3.1.1 --- Motivation --- p.21Chapter 3.1.2 --- Model --- p.23Chapter 3.1.3 --- Relevance Feedback --- p.24Chapter 3.1.4 --- Maximum Entropy Display --- p.26Chapter 3.2 --- Self-Organizing Map Based Inter-Query Feedback --- p.27Chapter 3.2.1 --- Motivation --- p.27Chapter 3.2.2 --- Initialization and Replication of SOM --- p.29Chapter 3.2.3 --- SOM Training for Inter-query Feedback --- p.31Chapter 3.2.4 --- Target Estimation and Display Set Selection for Intra- query Feedback --- p.33Chapter 3.3 --- Experiment --- p.35Chapter 3.3.1 --- Study of Parameter Estimation Method Using Synthetic Data --- p.35Chapter 3.3.2 --- Performance Study in Intra- and Inter- Query Feedback . --- p.40Chapter 3.4 --- Conclusion --- p.42Chapter 4 --- Distributed COntent-based Visual Information Retrieval --- p.44Chapter 4.1 --- Introduction --- p.44Chapter 4.2 --- Peer Clustering --- p.45Chapter 4.2.1 --- Basic Version --- p.45Chapter 4.2.2 --- Single Cluster Version --- p.47Chapter 4.2.3 --- Multiple Clusters Version --- p.51Chapter 4.3 --- Firework Query Model --- p.53Chapter 4.4 --- Implementation and System Architecture --- p.57Chapter 4.4.1 --- Gnutella Message Modification --- p.57Chapter 4.4.2 --- Architecture of DISCOVIR --- p.59Chapter 4.4.3 --- Flow of Operations --- p.60Chapter 4.5 --- Experiments --- p.62Chapter 4.5.1 --- Simulation Model of the Peer-to-Peer Network --- p.62Chapter 4.5.2 --- Number of Peers --- p.66Chapter 4.5.3 --- TTL of Query Message --- p.70Chapter 4.5.4 --- Effects of Data Resolution on Query Efficiency --- p.73Chapter 4.5.5 --- Discussion --- p.74Chapter 4.6 --- Conclusion --- p.77Chapter 5 --- Future Works and Conclusion --- p.79Chapter A --- Derivation of Update Equation --- p.81Chapter B --- An Efficient Discovery of Signatures --- p.82Bibliography --- p.8

The COST292 experimental framework for TRECVID 2007

In this paper, we give an overview of the four tasks submitted to TRECVID 2007 by COST292. In shot boundary (SB) detection task, four SB detectors have been developed and the results are merged using two merging algorithms. The framework developed for the high-level feature extraction task comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a Bayesian classifier trained with a “bag of subregions”. The third system uses a multi-modal classifier based on SVMs and several descriptors. The fourth system uses two image classifiers based on ant colony optimisation and particle swarm optimisation respectively. The system submitted to the search task is an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. Finally, the rushes task submission is based on a video summarisation and browsing system comprising two different interest curve algorithms and three features