Intelligent Image Retrieval Techniques: A Survey
TL;DR: The aim of this research is to highlight the efforts of researchers who have conducted some brilliant work and to provide a proof of concept for intelligent content-based image retrieval techniques.
Abstract: In the current era of digital communication, the use of digital images has increased for expressing, sharing andinterpreting information. While working with digital images, quite often it is necessary to search for a specific image for aparticular situation based on the visual contents of the image. This task looks easy if you are dealing with tens of imagesbut it gets more difficult when the number of images goes from tens to hundreds and thousands, and the same contentbasedsearching task becomes extremely complex when the number of images is in the millions. To deal with thesituation, some intelligent way of content-based searching is required to fulfill the searching request with right visualcontents in a reasonable amount of time. There are some really smart techniques proposed by researchers for efficientand robust content-based image retrieval. In this research, the aim is to highlight the efforts of researchers whoconducted some brilliant work and to provide a proof of concept for intelligent content-based image retrieval techniques.
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TL;DR: A comprehensive and structured review of the most relevant and recent unsupervised feature selection methods reported in the literature is provided and a taxonomy of these methods is presented.
Abstract: In recent years, unsupervised feature selection methods have raised considerable interest in many research areas; this is mainly due to their ability to identify and select relevant features without needing class label information. In this paper, we provide a comprehensive and structured review of the most relevant and recent unsupervised feature selection methods reported in the literature. We present a taxonomy of these methods and describe the main characteristics and the fundamental ideas they are based on. Additionally, we summarized the advantages and disadvantages of the general lines in which we have categorized the methods analyzed in this review. Moreover, an experimental comparison among the most representative methods of each approach is also presented. Finally, we discuss some important open challenges in this research area.
325 citations
02 May 2019
TL;DR: In this paper, the authors 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.
Abstract: 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 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.
211 citations
TL;DR: An overview of various methods for imageDenoising is given here after a brief introduction, these methods have been categorized on the bases of techniques used.
Abstract: Image Denoising is one of the fundamental and very important necessary processes in image processing. It is still a challenging and a hot problem for researchers. Images are one of essential representations in every field like education, agriculture, geosciences, aerospace, surveillance, entertainment etc by means of electronic or print media. Images can get corrupted by noise, there has been a great research effort which made solutions for this problem, a number of methods have been proposed. An overview of various methods is given here after a brief introduction. These methods have been categorized on the bases of techniques used.
86 citations
Cites methods from "Intelligent Image Retrieval Techniq..."
...Therefore noise removal from corrupted images is very important and necessary before further processing on them like segmentation [9-12], feature matching, edge detection, feature extraction [13-15], feature detection [16] of image details used for face recognition [17-38] etc, These denoised images can be used for face detection [39-41], content based image retrieval [42-48], medical image reconstruction [49], understanding Morphology of medical images [50] with its applications [51], medical image enhancement [52], image rendering [53] etc....
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TL;DR: A novel method for fuzzy medical image retrieval (FMIR) using vector quantization (VQ) with fuzzy signatures in conjunction with fuzzy S-trees is presented to help to determine appropriate healthcare according to the experiences of similar, previous cases.
Abstract: The aim of the article is to present a novel method for fuzzy medical image retrieval (FMIR) using vector quantization (VQ) with fuzzy signatures in conjunction with fuzzy S-trees. In past times, a task of similar pictures searching was not based on searching for similar content (e.g. shapes, colour) of the pictures but on the picture name. There exist some methods for the same purpose, but there is still some space for development of more efficient methods. The proposed image retrieval system is used for finding similar images, in our case in the medical area --- in mammography, in addition to the creation of the list of similar images --- cases. The created list is used for assessing the nature of the finding --- whether the medical finding is malignant or benign. The suggested method is compared to the method using Normalized Compression Distance (NCD) instead of fuzzy signatures and fuzzy S-tree. The method with NCD is useful for the creation of the list of similar cases for malignancy assessment, but it is not able to capture the area of interest in the image. The proposed method is going to be added to the complex decision support system to help to determine appropriate healthcare according to the experiences of similar, previous cases.
64 citations
Cites background from "Intelligent Image Retrieval Techniq..."
...Primarily, there are two ways how to search for similar images: querying the images is based on the keyword TBIR (Text-Base Image Retrieval) [45, 62] and based on the content CBIR (Content-Base Image retrieval) [13, 74]....
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TL;DR: The most successful approach in the CBIR framework is to use LLC for Coil20 data set and FBSR for Corel1000 data set, and three methods recently proposed in literature (Online Dictionary Learning, Locality-constrained Linear Coding and Feature-based Sparse Representation) are tested and compared with the framework results.
Abstract: Content Based Image Retrieval (CBIR) has been widely studied in the last two decades. Unlike text based image retrieval techniques, visual properties of images are used to obtain high level semantic information in CBIR. There is a gap between low level features and high level semantic information. This is called semantic gap and it is the most important problem in CBIR. The visual properties were extracted from low level features such as color, shape, texture and spatial information in early days. Local Feature Descriptors (LFDs) are more successful to increase performance of CBIR system. Then, a semantic bridge is built with high level semantic information. Sparse Representations (SRs) have become popular to achieve this aim in the last years. In this study, CBIR models that use LFDs and SRs in literature are investigated in detail. The SRs and LFD extraction algorithms are tested and compared within a CBIR framework for different scenarios. Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), Histograms of Oriented Gradients (HoG), Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) are used to extract LFDs from images. Random Features, K-Means and K-Singular Value Decomposition (K-SVD) algorithms are used for dictionary learning and Orthogonal Matching Pursuit (OMP), Homotopy, Lasso, Elastic Net, Parallel Coordinate Descent (PCD) and Separable Surrogate Function (SSF) are used for coefficient learning. Finally, three methods recently proposed in literature (Online Dictionary Learning (ODL), Locality-constrained Linear Coding (LLC) and Feature-based Sparse Representation (FBSR)) are also tested and compared with our framework results. All test results are presented and discussed. As a conclusion, the most successful approach in our framework is to use LLC for Coil20 data set and FBSR for Corel1000 data set. We obtain 89% and 58% Mean Average Precision (MAP) for Coil20 and Corel1000, respectively.
60 citations
References
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TL;DR: A relevance feedback based interactive retrieval approach that effectively takes into account the subjectivity of human perception of visual content and the gap between high-level concepts and low-level features in CBIR.
Abstract: Content-based image retrieval (CBIR) has become one of the most active research areas in the past few years. Many visual feature representations have been explored and many systems built. While these research efforts establish the basis of CBIR, the usefulness of the proposed approaches is limited. Specifically, these efforts have relatively ignored two distinct characteristics of CBIR systems: (1) the gap between high-level concepts and low-level features, and (2) the subjectivity of human perception of visual content. This paper proposes a relevance feedback based interactive retrieval approach, which effectively takes into account the above two characteristics in CBIR. During the retrieval process, the user's high-level query and perception subjectivity are captured by dynamically updated weights based on the user's feedback. The experimental results over more than 70000 images show that the proposed approach greatly reduces the user's effort of composing a query, and captures the user's information need more precisely.
1,933 citations
01 Jan 2001
1,388 citations
14 May 2006
TL;DR: A novel method for blind image restoration which is a multidimensional extension of an approach used successfully for audio restoration, and a maximum marginalised a posteriori (MMAP) blur estimate is obtained by optimising the resulting probability density function.
Abstract: We present a novel method for Blind image restoration which is a multidimensional extension of an approach used successfully for audio restoration. A nonstationary image model is used to increase reliability of blur estimates. This source model consists of a separate autoregressive model in each region of the image. A hierarchical Bayesian model for the observations is used, and a maximum marginalised a posteriori (MMAP) blur estimate is obtained by optimising the resulting probability density function.
1,132 citations
TL;DR: An asymmetric bagging and random subspace SVM (ABRS-SVM) is built to solve three problems and further improve the relevance feedback performance.
Abstract: Relevance feedback schemes based on support vector machines (SVM) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based relevance feedback is often poor when the number of labeled positive feedback samples is small. This is mainly due to three reasons: 1) an SVM classifier is unstable on a small-sized training set, 2) SVM's optimal hyperplane may be biased when the positive feedback samples are much less than the negative feedback samples, and 3) overfitting happens because the number of feature dimensions is much higher than the size of the training set. In this paper, we develop a mechanism to overcome these problems. To address the first two problems, we propose an asymmetric bagging-based SVM (AB-SVM). For the third problem, we combine the random subspace method and SVM for relevance feedback, which is named random subspace SVM (RS-SVM). Finally, by integrating AB-SVM and RS-SVM, an asymmetric bagging and random subspace SVM (ABRS-SVM) is built to solve these three problems and further improve the relevance feedback performance
916 citations
"Intelligent Image Retrieval Techniq..." refers methods in this paper
...A few approaches based on SVM (support vector machines) are discussed in [46-54]....
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...Relevance feedback system [22-122] was introduced to improve image retrieval performance and accuracy....
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...12 SVM based image retrieval techniques [46-54] Performance...
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Proceedings Article•
01 Jan 2007
TL;DR: A survey of CBIR systems is provided and the fundamental properties and techniques used in these systems are explained, including text-based information retrieval and why it does not work for searching through collections of images.
Abstract: With today’s large increase in digital images and automatically generated imagery, such as videos and stills generated from surveillance equipment, the need for efficient image retrieval and indexing has become fundamental. Since text-based information retrieval has been shown to perform very poorly when searching through images, research has been active in the field of content-based image retrieval (CBIR). CBIR systems make use of the properties of images in order to compare them and extract content by matching the query image. Comparing features – such as color, texture, and shape – allows for better retrieval accuracy; however, the algorithms used are still very limited. This paper will provide a survey of CBIR systems and explain the fundamental properties and techniques used in these systems. First, the history of CBIR systems will be discussed together with some typical CBIR systems. After this, the paper will touch on text-based information retrieval and explain why it does not work for searching through collections of images. The latter portion of this document will provides an overview of a typical CBIR system and the main techniques involved in querying such a system. Finally, image features and indexing schemes will be described.
511 citations