14,017 research outputs found
Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark
Psychological research results have confirmed that people can have different
emotional reactions to different visual stimuli. Several papers have been
published on the problem of visual emotion analysis. In particular, attempts
have been made to analyze and predict people's emotional reaction towards
images. To this end, different kinds of hand-tuned features are proposed. The
results reported on several carefully selected and labeled small image data
sets have confirmed the promise of such features. While the recent successes of
many computer vision related tasks are due to the adoption of Convolutional
Neural Networks (CNNs), visual emotion analysis has not achieved the same level
of success. This may be primarily due to the unavailability of confidently
labeled and relatively large image data sets for visual emotion analysis. In
this work, we introduce a new data set, which started from 3+ million weakly
labeled images of different emotions and ended up 30 times as large as the
current largest publicly available visual emotion data set. We hope that this
data set encourages further research on visual emotion analysis. We also
perform extensive benchmarking analyses on this large data set using the state
of the art methods including CNNs.Comment: 7 pages, 7 figures, AAAI 201
Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks
Sentiment analysis of online user generated content is important for many
social media analytics tasks. Researchers have largely relied on textual
sentiment analysis to develop systems to predict political elections, measure
economic indicators, and so on. Recently, social media users are increasingly
using images and videos to express their opinions and share their experiences.
Sentiment analysis of such large scale visual content can help better extract
user sentiments toward events or topics, such as those in image tweets, so that
prediction of sentiment from visual content is complementary to textual
sentiment analysis. Motivated by the needs in leveraging large scale yet noisy
training data to solve the extremely challenging problem of image sentiment
analysis, we employ Convolutional Neural Networks (CNN). We first design a
suitable CNN architecture for image sentiment analysis. We obtain half a
million training samples by using a baseline sentiment algorithm to label
Flickr images. To make use of such noisy machine labeled data, we employ a
progressive strategy to fine-tune the deep network. Furthermore, we improve the
performance on Twitter images by inducing domain transfer with a small number
of manually labeled Twitter images. We have conducted extensive experiments on
manually labeled Twitter images. The results show that the proposed CNN can
achieve better performance in image sentiment analysis than competing
algorithms.Comment: 9 pages, 5 figures, AAAI 201
A Data-Aided Channel Estimation Scheme for Decoupled Systems in Heterogeneous Networks
Uplink/downlink (UL/DL) decoupling promises more flexible cell association
and higher throughput in heterogeneous networks (HetNets), however, it hampers
the acquisition of DL channel state information (CSI) in time-division-duplex
(TDD) systems due to different base stations (BSs) connected in UL/DL. In this
paper, we propose a novel data-aided (DA) channel estimation scheme to address
this problem by utilizing decoded UL data to exploit CSI from received UL data
signal in decoupled HetNets where a massive multiple-input multiple-output BS
and dense small cell BSs are deployed. We analytically estimate BER performance
of UL decoded data, which are used to derive an approximated normalized mean
square error (NMSE) expression of the DA minimum mean square error (MMSE)
estimator. Compared with the conventional least square (LS) and MMSE, it is
shown that NMSE performances of all estimators are determined by their
signal-to-noise ratio (SNR)-like terms and there is an increment consisting of
UL data power, UL data length and BER values in the SNR-like term of DA method,
which suggests DA method outperforms the conventional ones in any scenarios.
Higher UL data power, longer UL data length and better BER performance lead to
more accurate estimated channels with DA method. Numerical results verify that
the analytical BER and NMSE results are close to the simulated ones and a
remarkable gain in both NMSE and DL rate can be achieved by DA method in
multiple scenarios with different modulations
???????????? ????????? ????????? ??????????????? ?????? ????????? ????????? ??????????????? ?????????
Department of Biomedical EngineeringImage stitching is a well-known method to make panoramic image which has a wide field-of-view and high resolution. It has been used in various fields such as digital map, gigapixel imaging, and 360-degree camera. However, commercial stitching tools often fail, require a lot of processing time, and only work on certain images. The problems of existing tools are mainly caused by trying to stitch the wrong image pair. To overcome these problems, it is important to select suitable image pair for stitching in advance. Nevertheless, there are no universal standards to judge the good image pairs. Moreover, the derived stitching algorithms cannot be compatible with each other because they conform to their own available criteria.
Here, we present universal stitching parameters and their conditions for selecting good image pairs. The proposed stitching parameters can be easily calculated through analysis of corresponding features and homography, which are basic elements in feature-based image stitching algorithm. In order to specify the conditions of the stitching parameters, we devised a new method to calculate stitching accuracy for qualifying stitching results into 3 classesgood, bad, and fail. With the classed stitching results, the values of the stitching parameters could be checked how they differ in each class. Through experiments with large datasets, the most valid parameter for each class is identified as filtering level which is calculated in corresponding feature analysis. In addition, supplemental experiments were conducted with various datasets to demonstrate the validity of the filtering level. As a result of our study, universal stitching parameters can judge the success of stitching, so that it is possible to prevent stitching errors through parameter verification test in advance. This paper can greatly contribute to guide for creating high performance and high efficiency stitching software by applying the proposed stitching conditions.ope
pH-Responsive Nanoparticles for Drug and Gene Delivery
Stimuli-sensitive polymers that respond to temperature, pH, light, ionic strength, and electric or magnetic fields are attractive for use in drug delivery, diagnostics, and sensing. Much attention has been paid to the use of pH-sensitive polymers for oral drug delivery, gene delivery, and insulin delivery, where a physiological pH shift facilitates swelling. In this research, pH-sensitive poly N,N-dimethylaminoethyl methacrylate (DMAEMA) / 2-hydroxyethyl methacrylate (HEMA) nanoparticles were prepared for the triggered release of paclitaxel within a tumor microenvironment. Monodispersed nanoparticles were synthesized by forming an O/W emulsion followed by photopolymerization. Particles were characterized by transmission electron microscopy, dynamic light scattering, electrophoresis, and cytotoxicity. High release rates and swelling ratios are achieved at low pH, low crosslinking density, and high content of DMAEMA. Cell viability studies indicate that pH-sensitive DMAEMA/HEMA nanoparticles are not cytotoxic and may be used as an efficient, feedback-regulated drug delivery carrier. DMAEMA/HEMA nanoparticles were also used as gene carriers with nearly 100% encapsulation rate due to their ability to swell at low pH which may overcome the obstacles of endosomal release and low transfection efficiency of nonviral gene delivery vectors. Once internalized via the endocytosis pathway, the nanoparticles undergo osmotic swelling facilitating endosomal escape of DNA to the cytoplasm. For intracellular tracking, particles were conjugated with quantum dots and examined by confocal microscopy at different time points. To examine the efficacy of DMAEMA/HEMA nanoparticles in facilitating endosomal escape of pDNA encoded EGFP, HeLa cells were incubated with DNA-loaded nanoparticles for 24 and 48 h. Enhanced gene transfection and particle uptake were achieved by modulating matrix elasticity and pH sensitivity. After a 48 h incubation, the transfection efficiency obtained by using DNA-loaded DMAEMA/HEMA nanoparticles was 45 ± 4.8%, in comparison to 40 ± 3.1% for PEI/DNA complexes. Our results show that elastic and pH-sensitive DMAEMA/HEMA nanoparticles enable higher gene expression at 24 and 48 h than polyethyleneimine (PEI)/DNA and poly-L-lysine (PLL)/DNA complexes without the drawback of cytotoxicity
- …