The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments
By Ingemar Johansson Cox, Matthew L. Miller, Tom Minka, T. Papathomas, P. Yianilos. IEEE Transactions on Image Processing, 2000
833 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/a60d352b1477fb9cd650510e3185104d82596221
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Li Fei-Fei
- Kristen Grauman
- Serge J. Belongie
- Trevor Darrell
- Devi Parikh
- Pietro Perona
- Bernt Schiele
- Abhinav Kumar Gupta
In Computer Vision and Pattern Recognition
- Dacheng Tao
- Luc Van Gool
- Trevor Darrell
- Li Fei-Fei
- Xiaogang Wang
- Kristen Grauman
- Jitendra Malik
- Ming-Hsuan Yang
Citations in the corpus
- Image retrieval: Ideas, influences, and trends of the new age (2008, neutral): “Probabilistic approaches have been taken in [Cox et al. 2000; Su et al. 2003; Vasconcelos and Lippman 2000a].”
- Learning the semantics of words and pictures (2001, methodology): “This work includes search by text [1, 2], search by image feature similarity [3-6], search by segment features [7], search for specific types of images using more compressive methods [8, 9], and search by image sketch [1].”
- Image retrieval via probabilistic hypergraph ranking (2010, background): “Previous work on relevance feedback often aims at learning discriminative models to classify the relevant and irrelevant images, such as, RF methods based on support vector machines (SVM) [29], decision trees [22], boosting [28], Bayesian classifiers [9], and graph-cut [25].”
- CLUE: cluster-based retrieval of images by unsupervised learning (2005, neutral): “Digital Object Identifier 10.1109/TIP.2005.849770”
- A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval (2002, methodology): “Content-based image retrieval (CBIR) is aimed at eÆcient retrieval of relevant images from large image databases based on automatically derived imagery features.”
- The Ignorant Led by the Blind: A Hybrid Human–Machine Vision System for Fine-Grained Categorization (2014, background): “2011; Parikh and Grauman 2013) and the use of information theoretic techniques to select which type of query to pose to the human user (Ferecatu and Geman 2007, 2009; Cox et al. 2000) (see Sect.”
- WhittleSearch: Image search with relative attribute feedback (2012, methodology): “Typically, a user refines the retrieved images via binary feedback on exemplars deemed “relevant” or “irrelevant” [14, 3, 22, 30, 7], or else attempts to tune system parameters such as weights on a small set of low-level features (e.”
- Learning to rank for information retrieval (2009, neutral): “[4] G.”
- A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video (2002, methodology): “[28] that is based on a Bayesian framework which is used to capture the relevance feedback to improve search results.”
- Towards Conversational Recommender Systems (2016, background): “The problem of eliciting user feedback has long been of interest for a variety of tasks (e.g [8]).”
- Content-based image retrieval: approaches and trends of the new age (2005, neutral): “Probabilistic approaches have been taken in [21, 91, 100].”
- Skeleton based shape matching and retrieval (2003, methodology): “In [13], an interactive algorithm is presented which employs a mechanism called " relevance feedback " to iteratively refine a search according to user preferences [11].”