An image retrieval system is a computer system used for browsing, searching and retrieving images from a large database of digital images. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning, keywords, title or descriptions to the images so that retrieval can be performed over the annotation words. Manual image annotation is time-consuming, laborious and expensive; to address this, there has been a large amount of research done on automatic image annotation. Additionally, the increase in social web applications and the semantic web have inspired the development of several web-based image annotation tools.
Content-based image retrieval, also known as query by image content and content-based visual information retrieval (CBVIR), is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases. Content-based image retrieval is opposed to traditional concept-based approaches.
Michael S. Lew is a scientist in multimedia information search and retrieval at Leiden University, Netherlands. He has published over a dozen books and 150 scientific articles in the areas of content based image retrieval, computer vision, and deep learning. Notably, he had the most cited paper in the ACM Transactions on Multimedia, one of the top 10 most cited articles in the history of the ACM SIGMM, and the most cited article from the ACM International Conference on Multimedia Information Retrieval in 2008 and also in 2010. He was the opening keynote speaker for the 9th International Conference on Visual Information Systems, the Editor-in-Chief of the International Journal of Multimedia Information Retrieval (Springer), the co-founder of influential conferences such as the International Conference on Image and Video Retrieval, and the IEEE Workshop on Human Computer Interaction. He was also a founding member of the international advisory committee for the TRECVID video retrieval evaluation project, chair of the steering committee for the ACM International Conference on Multimedia Retrieval and a member of the ACM SIGMM Executive Committee. In addition, his work on convolutional fusion networks in deep learning won the best paper award at the 23rd International Conference on Multimedia Modeling. His work is frequently cited in both scientific and popular news sources.
Query expansion (QE) is the process of reformulating a given query to improve retrieval performance in information retrieval operations, particularly in the context of query understanding. In the context of search engines, query expansion involves evaluating a user's input and expanding the search query to match additional documents. Query expansion involves techniques such as:
ACM Multimedia (ACM-MM) is the Association for Computing Machinery (ACM)'s annual conference on multimedia, sponsored by the SIGMM special interest group on multimedia in the ACM. SIGMM specializes in the field of multimedia computing, from underlying technologies to applications, theory to practice, and servers to networks to devices.
James Ze Wang is a Chinese-American computer scientist. He is a distinguished professor of the College of Information Sciences and Technology at Pennsylvania State University. He is also an affiliated professor of the Molecular, Cellular, and Integrative Biosciences Program; the Computational Science Graduate Minor; and the Social Data Analytics Graduate Program. He is co-director of the Intelligent Information Systems Laboratory. He was a visiting professor of the Robotics Institute at Carnegie Mellon University from 2007 to 2008. In 2011 and 2012, he served as a program manager in the Office of International Science and Engineering at the National Science Foundation. He is the second son of Chinese mathematician Wang Yuan.
In computer vision, the bag-of-words model sometimes called bag-of-visual-words model can be applied to image classification or retrieval, by treating image features as words. In document classification, a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary. In computer vision, a bag of visual words is a vector of occurrence counts of a vocabulary of local image features.
Caltech 101 is a data set of digital images created in September 2003 and compiled by Fei-Fei Li, Marco Andreetto, Marc 'Aurelio Ranzato and Pietro Perona at the California Institute of Technology. It is intended to facilitate computer vision research and techniques and is most applicable to techniques involving image recognition classification and categorization. Caltech 101 contains a total of 9,146 images, split between 101 distinct object categories and a background category. Provided with the images are a set of annotations describing the outlines of each image, along with a Matlab script for viewing.
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.
Global Memory Net (GMNet) is a world digital library of cultural, historical, and heritage image collections. It is directed by Ching-chih Chen, Professor Emeritus of Simmons College, Boston, Massachusetts and supported by the National Science Foundation (NSF)'s International Digital Library Program (IDLP). The goal of GMNet is to provide a global collaborative network that provides universal access to educational resources to a worldwide audience. GMNet provides multilingual and multimedia content and retrieval, as well as links directly to major resources, such as OCLC, Internet Archive, Million Book Project, and Google.
Learning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. Training data may, for example, consist of lists of items with some partial order specified between items in each list. This order is typically induced by giving a numerical or ordinal score or a binary judgment for each item. The goal of constructing the ranking model is to rank new, unseen lists in a similar way to rankings in the training data.
Multilinear principal component analysis (MPCA) is a multilinear extension of principal component analysis (PCA) that is used to analyze M-way arrays, also informally referred to as "data tensors". M-way arrays may be modeled by linear tensor models, such as CANDECOMP/Parafac, or by multilinear tensor models, such as multilinear principal component analysis (MPCA) or multilinear independent component analysis (MICA). The origin of MPCA can be traced back to the tensor rank decomposition introduced by Frank Lauren Hitchcock in 1927; to the Tucker decomposition; and to Peter Kroonenberg's "3-mode PCA" work. In 2000, De Lathauwer et al. restated Tucker and Kroonenberg's work in clear and concise numerical computational terms in their SIAM paper entitled "Multilinear Singular Value Decomposition", (HOSVD) and in their paper "On the Best Rank-1 and Rank-(R1, R2, ..., RN ) Approximation of Higher-order Tensors".
Shih-Fu Chang is a Taiwanese American computer scientist and electrical engineer noted for his research on multimedia information retrieval, computer vision, machine learning, and signal processing.
Jiebo Luo is a Chinese-American computer scientist, the Albert Arendt Hopeman Professor of Engineering and Professor of Computer Science at the University of Rochester. He is interested in artificial intelligence, data science and computer vision.
Michael J. Black is an American-born computer scientist working in Tübingen, Germany. He is a founding director at the Max Planck Institute for Intelligent Systems where he leads the Perceiving Systems Department in research focused on computer vision, machine learning, and computer graphics. He is also an Honorary Professor at the University of Tübingen.
Song-Chun Zhu is a Chinese computer scientist and applied mathematician known for his work in computer vision, cognitive artificial intelligence and robotics. Zhu currently works at Peking University and was previously a professor in the Departments of Statistics and Computer Science at the University of California, Los Angeles. Zhu also previously served as Director of the UCLA Center for Vision, Cognition, Learning and Autonomy (VCLA).
Edward Y. Chang is a computer scientist, academic, and author. He is an adjunct professor of Computer Science at Stanford University, and Visiting Chair Professor of Bioinformatics and Medical Engineering at Asia University, since 2019.
Curriculum learning is a technique in machine learning in which a model is trained on examples of increasing difficulty, where the definition of "difficulty" may be provided externally or discovered automatically as part of the training process. This is intended to attain good performance more quickly, or to converge to a better local optimum if the global optimum is not found.
