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Interactive Content-Based Image Retrieval with Deep Neural Networks [chapter]

Joel Pyykkö, Dorota Głowacka
2017 Lecture Notes in Computer Science  
Recent advances in deep neural networks have given rise to new approaches to content-based image retrieval (CBIR).  ...  However, employing deep neural networks in interactive CBIR systems still poses challenges: either the search target has to be predetermined, such as with hashing, or the computational cost becomes prohibitive  ...  Conclusions We presented a deep neural network framework for learning new representations in an online interactive CBIR setting.  ... 
doi:10.1007/978-3-319-57753-1_7 fatcat:x7m6apypcramfec2m7dtlctbsa

Image retrieval and classification using affine invariant B-spline representation and neural networks

Y. Xirouhakis, Y. Avrithis, S. Kollias
1998 IEE Colloquium Neural Networks in Interactive Multimedia Systems   unpublished
A neural network approach is used for supervised classification of video objects into prototype object classes.  ...  In this paper, a system for content-based image retrieval from video databases is introduced, using B-splines for affine invariant object representation.  ...  As it will be seen in the sequel, the normalized Fourier descriptors are fed into a neural network (NN).  ... 
doi:10.1049/ic:19980712 fatcat:h5352dq24reopjk4sr3izewshi

Comparing neural and probabilistic relevance feedback in an interactive information retrieval system

F. Crestani
Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)  
This paper presents the results of an experimental investigation into the use of Neural Networks for implementing Relevance Feedback in an interactive Information Retrieval System.  ...  The most advance Relevance Feedback technique used in operative Interactive Information Retrieval systems, Probabilistic Relevance Feedback, is compared with a Neural Networks based technique.  ...  In this paper we will investigate the possibility of using Neural Networks (NN) in IR and in particular we will concentrate on the RF process.  ... 
doi:10.1109/icnn.1994.374787 fatcat:o4a3md7xsbdjpmmctx3blpcqca

Dynamic neural networks supporting memory retrieval

Peggy L. St. Jacques, Philip A. Kragel, David C. Rubin
2011 NeuroImage  
However, it is unclear how such large-scale neural networks interact and are modulated by properties of the memory retrieval process.  ...  In the present functional MRI (fMRI) study, we combined independent component analysis (ICA) and dynamic causal modeling (DCM) to understand the neural networks supporting AM retrieval.  ...  This research was supported by a National Institute of Aging RO1 AG023123 to DCR, and a post-doctoral NRSA AG038079 and L'Oreal USA for Women in Science Fellowship to PLS.  ... 
doi:10.1016/j.neuroimage.2011.04.039 pmid:21550407 pmcid:PMC3114167 fatcat:oehcjkwlbfgwpm6rwaf6leh7ce

A Partial Matching Convolution Neural Network for Source Retrieval of Plagiarism Detection

Leilei KONG, Yong HAN, Haoliang QI, Zhongyuan HAN
2021 IEICE transactions on information and systems  
In detail, PMCNN exploits a sequential convolution neural network to extract the plagiarism patterns of contiguous text segments.  ...  models. key words: plagiarism detection, source retrieval, partial matching, convolution neural network  ...  Addressing the partial matching in source retrieval, we propose PMCNN (Partial Matching Convolution Neural Network), a deep neural network architecture based on sequential convolution for source retrieval  ... 
doi:10.1587/transinf.2020edl8162 fatcat:6nczxjuf7zd6vkw4wmxq7a2vee

Neural Ranking Models for Document Retrieval [article]

Mohamed Trabelsi, Zhiyu Chen, Brian D. Davison, Jeff Heflin
2021 arXiv   pre-print
A variety of deep learning models have been proposed, and each model presents a set of neural network components to extract features that are used for ranking.  ...  Recently, researchers have leveraged deep learning models in information retrieval.  ...  Effective user interaction for high-recall retrieval: Less is more. In Proceedings of the 27th ACM International Conference on .  ... 
arXiv:2102.11903v1 fatcat:zc2otf456rc2hj6b6wpcaaslsa

Neu-IR

Nick Craswell, W. Bruce Croft, Jiafeng Guo, Bhaskar Mitra, Maarten de Rijke
2016 Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR '16  
Neu-IR: The SIGIR 2016 Workshop on Neural Information Retrieval Craswell, N.; Croft, W.B.; Guo, J.; Mitra, B.; de Rijke, M.  ...  We are interested in submissions relevant to the following main themes: • The application of neural network models in IR tasks, including but not limited to: -Full text document retrieval, passage retrieval  ...  neural network communities are only beginning to focus on the application of these techniques to core information retrieval problems.  ... 
doi:10.1145/2911451.2917762 dblp:conf/sigir/CraswellCGMR16 fatcat:rx3xtbfnnbahxa3axdhnoncyt4

Page 476 of Journal of Cognitive Neuroscience Vol. 17, Issue 3 [page]

2005 Journal of Cognitive Neuroscience  
There- fore, if a memory system is defined as a neural system that shows a unique memory-type-specific activation pattern and a functional interaction pattern, then the similarities in network interactions  ...  However, there were small differences in neural interactions across conditions.  ... 

A Deep Look into Neural Ranking Models for Information Retrieval [article]

Jiafeng Guo, Yixing Fan, Liang Pang, Liu Yang, Qingyao Ai, Hamed Zamani, Chen Wu, W. Bruce Croft, Xueqi Cheng
2019 arXiv   pre-print
Recently, with the advance of deep learning technology, we have witnessed a growing body of work in applying shallow or deep neural networks to the ranking problem in IR, referred to as neural ranking  ...  Neural networks have sufficient capacity to model complicated tasks, which is needed to handle the complexity of relevance estimation in ranking.  ...  [147] conducted empirical studies on the interaction-based neural ranking model to understand what have been learned in each neural network layer.  ... 
arXiv:1903.06902v3 fatcat:j22ic7foibcurp45b4amdiwfhu

Neural reactivation in parietal cortex enhances memory for episodically linked information

Tanya R. Jonker, Halle Dimsdale-Zucker, Maureen Ritchey, Alex Clarke, Charan Ranganath
2018 Proceedings of the National Academy of Sciences of the United States of America  
Using representational similarity analysis of fMRI data in experiment 2, we found that retrieval resulted in greater neural reactivation of both the target objects and contextually linked objects compared  ...  areas (also known as the Default Network) play complementary roles in supporting the reactivation of episodically linked information during retrieval.  ...  Reactivation of the target representation occurred in the PM network as well as in the posterior hippocampal regions, which is expected to preferentially interact with the PM network.  ... 
doi:10.1073/pnas.1800006115 pmid:30297400 pmcid:PMC6205442 fatcat:iuurnqcndnb3jic3vswx4xn2vq

Page 5129 of Psychological Abstracts Vol. 83, Issue 12 [page]

1996 Psychological Abstracts  
—Proposes a neural network model! that integrates visual information and somatosensory in- formation for preshaping a hand in grasping movements.  ...  satisfies the speed and convergence criteria and accounts for several major empirical findings on conceptually driven lemma retrieval Neural Networks Serials 38584.  ... 

High Order Neural Networks for Efficient Associative Memory Design

Gérard Dreyfus, Isabelle Guyon, Jean-Pierre Nadal, Léon Personnaz
1987 Neural Information Processing Systems  
We propose learning rules for recurrent neural networks with high-order interactions between some or all neurons.  ...  The designed networks exhibit the desired associative memory function: perfect storage and retrieval of pieces of information and/or sequences of information of any complexity.  ...  GENERALIZATION TASKS Apart from storing and retrieving static pieces of information or sequences, neural networks can be used to solve problems in which there exists a structure or regularity in the sample  ... 
dblp:conf/nips/DreyfusGNP87 fatcat:qrwf4kt7izezlid34fpovlagwm

A Neural-Network-Based Approach to Adaptive Human Computer Interaction [chapter]

George Votsis, Nikolaos D. Doulamis, Anastasios D. Doulamis, Nicolas Tsapatsoulis, Stefanos D. Kollias
2001 Lecture Notes in Computer Science  
A neural-network-based approach is proposed in this paper providing multimedia systems with the ability to adapt their performance to the specific needs and characteristics of their users.  ...  In the former, adaptive non-linear relevance feedback is proposed for content-based retrieval of multimedia information.  ...  In this paper we show that neural networks can form a crucial component of HCI systems responsible for content-based retrieval of multimedia information based on user relevance feedback [2] and for recognition  ... 
doi:10.1007/3-540-44668-0_146 fatcat:o5kmdsprazeoha2ch2shtrxd4u

Kansei Retrieval Model using a Neural Network

Daichi OKUNAKA, Masataka TOKUMARU
2012 Transactions of Japan Society of Kansei Engineering  
In this paper, we propose a Kansei Retrieval model using a neural network. It is very difficult to design Kansei Retrieval Agent to imitate a user's Kansei.  ...  To solve this problem, we use the neural network, which is comparatively easy to design. The neural network is a mathematical model that imitates brain functions.  ...  CD 1-3 Interactive Evolutionary Computation IEC 4 4 Neural Network NN 2 IEC 3 NN NN 4 5 2. 1 IEC IEC IEC 5-9 IEC 10 IEC IEC Kansei Retrieval Model using a Neural Network  ... 
doi:10.5057/jjske.11.331 fatcat:bp5c6ef4mbgwjlhz7lciyt7hne

Optimal pair of hippocampal CA1 phase response curve and spike-timing-dependent plasticity for hetero-associative memory

Ryota Miyata, Keisuke Ota, Toru Aonishi
2013 BMC Neuroscience  
In this paper, we explore the idea that the properties of the neural interactions and the synaptic plasticity rule in the CA1 network enable it to function as a hetero-associative memory recalling such  ...  In line with Lengyel's speculation [3], we derive optimally designed spike-timing-dependent plasticity (STDP) rules that are matched to neural interactions formalized in terms of phase response curves  ...  In this paper, we explore the idea that the properties of the neural interactions and the synaptic plasticity rule in the CA1 network enable it to function as a hetero-associative memory recalling such  ... 
doi:10.1186/1471-2202-14-s1-p9 pmcid:PMC3704853 fatcat:y4aqftf2g5fznbstza76kjd7ny
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