A 20th Anniversary Survey: Introduction to 'Content-Based Image Retrieval at the End of the Early Years'

By Kevin W. Bowyer, Patrick J. Flynn. 2000

4,535 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Oncology, Information Retrieval & Search

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Oncology

In Information Retrieval & Search

Citations in the corpus

A Multi-View Embedding Space for Modeling Internet Images, Tags, and Their Semantics (2012, methodology): “The two main tasks we use for evaluating our system are image-to-image search, which has been traditionally studied as content-based image retrieval (Datta et al., 2008; Smeulders et al., 2000), and tag-to-image search, or image retrieval using text-based queries (Grangier and Bengio, 2008; Krapac e”
A compact and efficient image retrieval approach based on border/interior pixel classification (2002, background): “These algorithms are usually distinct for different image domains, and gradually change when the focus moves from a narrow to a broad image domain [12].”
Boosting multi-kernel locality-sensitive hashing for scalable image retrieval (2012, methodology): “Similarity search, or Nearest Neighbor (NN) search, plays a critical role in Content-Based Image Retrieval (CBIR) systems [33, 24].”
A new approach to cross-modal multimedia retrieval (2010, background): “PREVIOUS WORK The problems of image and text retrieval have been the subject of extensive research in areas such as information retrieval, computer vision, and multimedia [6, 27, 26, 21, 18].”
Attribute feedback (2012, background): “INTRODUCTION With the growing volume of on-line images as well as the increasing requirements in many multimedia applications, Content Based Image Retrieval (CBIR) has attracted significant attention in both academia and industry [19, 26, 33].”
Image Retrieval from the World Wide Web: Issues, Techniques, and Systems (2004, methodology): “The lack of coincidence between the information that can be extracted from the visual data and the interpretation that a user assigns to the same data in a given situation is known as the semantic gap [Smeulders et al. 2000].”
Boosting Image Retrieval (2000, background): “Huang and Chang, 1999; Smeulders et al., 2000 )).”
Harvesting large-scale weakly-tagged image databases from the web (2010, background): “For many computer vision tasks, such as object detection and scene recognition, machine learning techniques are usually involved to learn the classifiers from a set of labeled training images [1].”
Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks (2016, background): “On the other hand, highly efficient descriptor enables machines to run in real-time, which is also important for retrieving image in a large corpus [37], or detecting objects with mobile devices [43, 50].”
Fuzzy spatial relation ontology for image interpretation (2008, background): “This problem is often referred to as the semantic gap, defined as “the lack of coincidence between the information that one can extract from the visual data and the interpretation that the same data have for a user in a given situation’’ [64].”