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Content-based image retrieval

About: Content-based image retrieval is a research topic. Over the lifetime, 6916 publications have been published within this topic receiving 150696 citations. The topic is also known as: CBIR.


Papers
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Proceedings ArticleDOI
06 Jun 2004
TL;DR: The main findings were that the creation process could be implemented with current technology and it facilitated the creation of semantic metadata at the time of image capture.
Abstract: The amount of personal digital media is increasing, and managing it has become a pressing problem. Effective management of media content is not possible without content-related metadata. In this paper we describe a content metadata creation process for images taken with a mobile phone. The design goals were to automate the creation of image content metadata by leveraging automatically available contextual metadata on the mobile phone, to use similarity processing algorithms for reusing shared metadata and images on a remote server, and to interact with the mobile phone user during image capture to confirm and augment the system supplied metadata. We built a prototype system to evaluate the designed metadata creation process. The main findings were that the creation process could be implemented with current technology and it facilitated the creation of semantic metadata at the time of image capture.

300 citations

Journal ArticleDOI
TL;DR: This paper presents a method to extract color and texture features of an image quickly for content-based image retrieval (CBIR), and shows that the fused features retrieval brings better visual feeling than the single feature retrieval, which means better retrieval results.

297 citations

Journal ArticleDOI
TL;DR: The retrieval using the MCM is better than the CCM since it captures the third order image statistics in the local neighborhood and the use of MCM considerably improves the retrieval performance.

293 citations

Journal ArticleDOI
TL;DR: Simulation results clearly show that the proposed invariant Gabor representations and their extracted invariant features significantly outperform the conventional Gabor representation approach for rotation-invariant and scale-Invariant texture image retrieval.

293 citations

Journal ArticleDOI
TL;DR: A new approach to content-based retrieval of medical images from a database is described, in which similarity is learned from training examples provided by human observers, and the use of neural networks and support vector machines to predict the user's notion of similarity is explored.
Abstract: In this paper, we describe an approach to content-based retrieval of medical images from a database, and provide a preliminary demonstration of our approach as applied to retrieval of digital mammograms. Content-based image retrieval (CBIR) refers to the retrieval of images from a database using information derived from the images themselves, rather than solely from accompanying text indices. In the medical-imaging context, the ultimate aim of CBIR is to provide radiologists with a diagnostic aid in the form of a display of relevant past cases, along with proven pathology and other suitable information. CBIR may also be useful as a training tool for medical students and residents. The goal of information retrieval is to recall from a database information that is relevant to the user's query. The most challenging aspect of CBIR is the definition of relevance (similarity), which is used to guide the retrieval machine. In this paper, we pursue a new approach, in which similarity is learned from training examples provided by human observers. Specifically, we explore the use of neural networks and support vector machines to predict the user's notion of similarity. Within this framework we propose using a hierarchal learning approach, which consists of a cascade of a binary classifier and a regression module to optimize retrieval effectiveness and efficiency. We also explore how to incorporate online human interaction to achieve relevance feedback in this learning framework. Our experiments are based on a database consisting of 76 mammograms, all of which contain clustered microcalcifications (MCs). Our goal is to retrieve mammogram images containing similar MC clusters to that in a query. The performance of the retrieval system is evaluated using precision-recall curves computed using a cross-validation procedure. Our experimental results demonstrate that: 1) the learning framework can accurately predict the perceptual similarity reported by human observers, thereby serving as a basis for CBIR; 2) the learning-based framework can significantly outperform a simple distance-based similarity metric; 3) the use of the hierarchical two-stage network can improve retrieval performance; and 4) relevance feedback can be effectively incorporated into this learning framework to achieve improvement in retrieval precision based on online interaction with users; and 5) the retrieved images by the network can have predicting value for the disease condition of the query.

291 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202358
2022141
2021180
2020163
2019224
2018270