Gap In Content-based Image Retrieval Research Articles

A comprehensive survey on the reduction of the semantic gap in content-based image retrieval

A comprehensive survey on the reduction of the semantic gap in content-based image retrieval

A comprehensive survey on the reduction of the semantic gap in content-based image retrieval

In the last few decades, content-based image retrieval is considered as one of the most vivid research topics in the field of information retrieval. The limitation of current content-based image retrieval systems is that low-level features are highly ineffective to represent the semantic contents of the image. Most of the research work in content-based image retrieval is focused on bridging the semantic gap between the low-level features and high-level semantic concepts of image. This paper presents a thorough study of different techniques for the reduction of semantic gap. The existing techniques are broadly categorised as: 1) image annotation techniques to define the high-level concepts in image; 2) relevance feedback techniques to integrate user's perception; 3) machine learning and deep learning techniques to associate low-level features with high-level concepts. In addition, the general architecture of semantic-based image retrieval system has been discussed in this survey. This paper also highlights the current and future applications of content-based image retrieval. The paper concludes with promising future research directions.

Review of the application of machine learning to the automatic semantic annotation of images

The massive amount of digital content generated daily in the modern world has created the need for an image retrieval system built on image analysis via image processing and machine learning, therefore this study explains the role of machine learning in bridging the semantic gap in content-based image retrieval, proposes an automatic image annotation framework, in which training images are obtained from social media, and semantic indexing is achieved using a combination of supervised and unsupervised machine learning. Furthermore, the study also highlights the need for continuous vocabulary improvement for optimum system performance and recommends hardware implementation of machine learning algorithms to ensure high overall speed of image retrieval systems.

Content-Based Image Retrieval : A Comprehensive Study

Learning efficient options illustrations and equivalency metric measures are imperative to the searching performance of a content-based image retrieval (CBIR) machine. Despite in depth analysis efforts for many years, it remains one amongst the foremost difficult open issues that significantly hinders the success of real- world CBIR systems. The key issue has been associated to the commonly known “linguistic gap” problem that exists between low-level image pixels captured by machines and high-level linguistics ideas perceived by humans. Among varied techniques, machine learning has been actively investigated as a potential direction to bridge the linguistics gap in the long run. Motivated by recent success of deep learning techniques for computer vision and other applications, In this paper, we'll conceive to address an open problem: if deep learning could be a hope for bridging the linguistics gap in CBIR and the way a lot of enhancements in CBIR tasks may be achieved by exploring the progressive deep learning methodologies for learning options illustrations and equivalency measures. Speci?cally, we'll investigate a framework of deep learning with application to CBIR tasks with an extensive set of empirical studies by examining a progressive deep learning technique (Convolutional Neural Networks) for CBIR tasks in varied settings. From our empirical studies, we found some encouraging results and summarized some vital insights for future analysis. CBIR tasks may be achieved by exploring the progressive deep learning techniques for learning options illustrations and equivalency measures.

Saliency-based multi-feature modeling for semantic image retrieval

Semantic gap is an important challenging problem in content-based image retrieval (CBIR) up to now. Bag-of-words (BOW) framework is a popular approach that tries to reduce the semantic gap in CBIR. In this paper, an approach integrating visual saliency model with BOW is proposed for semantic image retrieval. Images are firstly segmented into background regions and foreground objects by a visual saliency-based segmentation method. And then multi-features including Scale Invariant Feature Transform (SIFT) features packed in BOW are extracted from regions and objects respectively and fused considering different characteristics of background regions and foreground objects. Finally, a fusion of z-score normalized Chi-Square distance is adopted as the similarity measurement. This proposal has been implemented on two widely used benchmark databases and the results evaluated in terms of mean Average Precision (mAP) show that our proposal outperforms the referred state-of-the-art approaches.

Reduce Semantic Gap in Content-Based Image Retrieval

Reduce Semantic Gap in Content-Based Image Retrieval

Content-based Retrieval of Medical Images: Landmarking, Indexing, and Relevance Feedback

Content-based image retrieval (CBIR) is the process of retrieval of images from a database that are similar to a query image, using measures derived from the images themselves, rather than relying on accompanying text or annotation. To achieve CBIR, the contents of the images need to be characterized by quantitative features; the features of the query image are compared with the features of each image in the database and images having high similarity with respect to the query image are retrieved and displayed. CBIR of medical images is a useful tool and could provide radiologists with assistance in the form of a display of relevant past cases. One of the challenging aspects of CBIR is to extract features from the images to represent their visual, diagnostic, or application-specific information content. In this book, methods are presented for preprocessing, segmentation, landmarking, feature extraction, and indexing of mammograms for CBIR. The preprocessing steps include anisotropic di fusion and the Wiener filter to remove noise and perform image enhancement. Techniques are described for segmentation of the breast and fibroglandular disk, including maximum entropy, a moment-preserving method, and Otsu's method. Image processing techniques are described for automatic detection of the nipple and the edge of the pectoral muscle via analysis in the Radon domain. By using the nipple and the pectoral muscle as landmarks, mammograms are divided into their internal, external, upper, and lower parts for further analysis. Methods are presented for feature extraction using texture analysis, shape analysis, granulometric analysis, moments, and statistical measures. The CBIR system presented provides options for retrieval using the Kohonen self-organizing map and the k-nearest-neighbor method. Methods are described for inclusion of expert knowledge to reduce the semantic gap in CBIR, including the query point movement method for relevance feedback (RFb). Analysis of performanc is described in terms of precision, recall, and relevance-weighted precision of retrieval. Results of application to a clinical database of mammograms are presented, including the input of expert radiologists into the CBIR and RFb processes. Models are presented for integration of CBIR and computer-aided diagnosis (CAD) with a picture archival and communication system (PACS) for efficient workflow in a hospital. Table of Contents: Introduction to Content-based Image Retrieval / Mammography and CAD of Breast Cancer / Segmentation and Landmarking of Mammograms / Feature Extraction and Indexing of Mammograms / Content-based Retrieval of Mammograms / Integration of CBIR and CAD into Radiological Workflow

A noise-resilient collaborative learning approach to content-based image retrieval

We propose to combine short-term block-based fuzzy support vector machine (FSVM) learning and long-term dynamic semantic clustering (DSC) learning to bridge the semantic gap in content-based image retrieval. The short-term learning addresses the small sample problem by incorporating additional image blocks to enlarge the training set. Specifically, it applies the nearest neighbor mechanism to choose additional similar blocks. A fuzzy metric is computed to measure the fidelity of the actual class information of the additional blocks. The FSVM is finally applied on the enlarged training set to learn a more accurate decision boundary for classifying images. The long-term learning addresses the large storage problem by building dynamic semantic clusters to remember the semantics learned during all query sessions. Specifically, it applies a cluster-image weighting algorithm to find the images most semantically related to the query. It then applies a DSC technique to adaptively learn and update the semantic categories. Our extensive experimental results demonstrate that the proposed short-term, long-term, and collaborative learning methods outperform their peer methods when the erroneous feedback resulting from the inherent subjectivity of judging relevance, user laziness, or maliciousness is involved. The collaborative learning system achieves better retrieval precision and requires significantly less storage space than its peers. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

How to Cope with the Performance Gap in Content-Based Image Retrieval Systems

This paper discusses the main aspects regarding the performance gap in Content-based Image Retrieval (CBIR) systems, which is an important issue regarding their acceptability. We also detail the main problems that lead to the performance gap: the use of many features to represent images, the lack of appropriate indexing structures for images and features, deficient query plans employed to execute similarity queries, and sometimes the poor quality of results obtained by the CBIR system. We present guidelines to overcome these problems by employing feature selection techniques to beat the "dimensionality curse", by using proper access methods to support fast and effective indexing and retrieval of images, by stressing the importance of using query optimization approaches and by including the user during the tuning of the CBIR system through relevance feedback techniques.

Reducing the semantic gap in content-based image retrieval in mammography with relevance feedback and inclusion of expert knowledge

We investigate the use of relevance feedback (RFb) and the inclusion of expert knowledge to reduce the semantic gap in content-based image retrieval (CBIR) of mammograms. Tests were conducted with radiologists, in which their judgment of the relevance of the retrieved images was used with techniques of query-point movement to incorporate RFb. The measures of similarity of images used for CBIR were based upon textural characteristics and the distribution of density of fibroglandular tissue in the breast. The features used include statistics of the gray-level histogram, texture features based upon the gray-level co-occurrence matrix, moment-based features, measures computed in the Radon domain, and granulometric measures. Queries for CBIR with RFb were executed by three radiologists. The performance of CBIR was measured in terms of precision of retrieval and a measure of relevance-weighted precision (RWP) of retrieval. The results indicate improvement due to RFb of up to 62% in precision and 39% in RWP. The gain in performance of CBIR with RFb depended upon the BI-RADS breast density index of the query mammographic image, with greater improvement in cases of mammograms with higher density.

DRM: dynamic region matching for image retrieval using probabilistic fuzzy matching and boosting feature selection

This paper considers the semantic gap in content-based image retrieval from two aspects: (1) irrelevant visual contents (e.g. background) scatter the mapping from image to human perception; (2) unsupervised feature extraction and similarity ranking method can not accurately reveal users’ image perception. This paper proposes a novel region-based retrieval framework—dynamic region matching (DRM) to bridge the semantic gap. (1) To address the first issue, a probabilistic fuzzy region matching algorithm is adopted to retrieve and match images precisely at object level, which copes with the problem of inaccurate segmentation. (2) To address the second issue, a “FeatureBoost” algorithm is proposed to construct an effective “eigen” feature set in relevance feedback (RF) process. And the significance of each region is dynamically updated in RF learning to automatically capture users’ region of interest (ROI). (3) User’s retrieval purpose is predicted using a novel log-learning algorithm, which predicts users’ retrieval target in the feature space using the accumulated user operations. Extensive experiments have been conducted on Corel image database with over 10,000 images. The promising experimental results reveal the effectiveness of our scheme in bridging the semantic gap.