Journal ArticleDOI
Information fusion in content based image retrieval
Luca Piras,Giorgio Giacinto +1 more
TLDR
A journey through the main information fusion ingredients that a recipe for the design of a CBIR system should include to meet the demanding needs of users is offered.About:
This article is published in Information Fusion.The article was published on 2017-09-01. It has received 106 citations till now. The article focuses on the topics: Content-based image retrieval & Image retrieval.read more
Citations
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Journal ArticleDOI
Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review
Afshan Latif,Aqsa Rasheed,Umer Sajid,Jameel Ahmed,Nouman Ali,Naeem Iqbal Ratyal,Bushra Zafar,Bushra Zafar,Saadat Hanif Dar,Muhammad Sajid,Tehmina Khalil +10 more
TL;DR: A comprehensive review of the recent development in the area of CBIR and image representation is presented and the main aspects of various image retrieval and image representations models from low-level feature extraction to recent semantic deep-learning approaches are analyzed.
Journal ArticleDOI
Multi-source information fusion based on rough set theory: A review
Pengfei Zhang,Tianrui Li,Guoqiang Wang,Chuan Luo,Hongmei Chen,Junbo Zhang,Dexian Wang,Zeng Yu +7 more
TL;DR: This survey will directly help researchers understand the research developments of MSIF under RST and provide state-of-the-art understanding in specialized literature, as well as clarify the approaches and application of MSif in RST research community.
Journal ArticleDOI
Cognitive-inspired class-statistic matching with triple-constrain for camera free 3D object retrieval
TL;DR: A cognitive-inspired class-statistics matching method with triple-constraint (CSTC) for camera free 3D object retrieval that outperforms or is comparable with the-state-of-the-art algorithms, but the retrieval speed obviously outperforms others.
Journal ArticleDOI
TF-YOLO: An Improved Incremental Network for Real-Time Object Detection
TL;DR: Experimental results demonstrate that the proposed TF-YOLO method is a smaller, faster and more efficient network model increasing the performance of end-to-end training and real-time object detection for a variety of devices.
Journal ArticleDOI
Multimodal feature fusion by relational reasoning and attention for visual question answering
TL;DR: It is shown that combining visual relationship and attention together achieves more fine-grained feature fusion, and an effective and efficient module to reason complex relationship between visual objects is designed.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal ArticleDOI
ImageNet classification with deep convolutional neural networks
TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.