50 research outputs found
Probability Weighted Compact Feature for Domain Adaptive Retrieval
Domain adaptive image retrieval includes single-domain retrieval and
cross-domain retrieval. Most of the existing image retrieval methods only focus
on single-domain retrieval, which assumes that the distributions of retrieval
databases and queries are similar. However, in practical application, the
discrepancies between retrieval databases often taken in ideal
illumination/pose/background/camera conditions and queries usually obtained in
uncontrolled conditions are very large. In this paper, considering the
practical application, we focus on challenging cross-domain retrieval. To
address the problem, we propose an effective method named Probability Weighted
Compact Feature Learning (PWCF), which provides inter-domain correlation
guidance to promote cross-domain retrieval accuracy and learns a series of
compact binary codes to improve the retrieval speed. First, we derive our loss
function through the Maximum A Posteriori Estimation (MAP): Bayesian
Perspective (BP) induced focal-triplet loss, BP induced quantization loss and
BP induced classification loss. Second, we propose a common manifold structure
between domains to explore the potential correlation across domains.
Considering the original feature representation is biased due to the
inter-domain discrepancy, the manifold structure is difficult to be
constructed. Therefore, we propose a new feature named Histogram Feature of
Neighbors (HFON) from the sample statistics perspective. Extensive experiments
on various benchmark databases validate that our method outperforms many
state-of-the-art image retrieval methods for domain adaptive image retrieval.
The source code is available at https://github.com/fuxianghuang1/PWCFComment: Accepted by CVPR 2020; The source code is available at
https://github.com/fuxianghuang1/PWC
BiGSeT: Binary Mask-Guided Separation Training for DNN-based Hyperspectral Anomaly Detection
Hyperspectral anomaly detection (HAD) aims to recognize a minority of
anomalies that are spectrally different from their surrounding background
without prior knowledge. Deep neural networks (DNNs), including autoencoders
(AEs), convolutional neural networks (CNNs) and vision transformers (ViTs),
have shown remarkable performance in this field due to their powerful ability
to model the complicated background. However, for reconstruction tasks, DNNs
tend to incorporate both background and anomalies into the estimated
background, which is referred to as the identical mapping problem (IMP) and
leads to significantly decreased performance. To address this limitation, we
propose a model-independent binary mask-guided separation training strategy for
DNNs, named BiGSeT. Our method introduces a separation training loss based on a
latent binary mask to separately constrain the background and anomalies in the
estimated image. The background is preserved, while the potential anomalies are
suppressed by using an efficient second-order Laplacian of Gaussian (LoG)
operator, generating a pure background estimate. In order to maintain
separability during training, we periodically update the mask using a robust
proportion threshold estimated before the training. In our experiments, We
adopt a vanilla AE as the network to validate our training strategy on several
real-world datasets. Our results show superior performance compared to some
state-of-the-art methods. Specifically, we achieved a 90.67% AUC score on the
HyMap Cooke City dataset. Additionally, we applied our training strategy to
other deep network structures, achieving improved detection performance
compared to their original versions, demonstrating its effective
transferability. The code of our method will be available at
https://github.com/enter-i-username/BiGSeT.Comment: 13 pages, 13 figures, submitted to IEEE TRANSACTIONS ON IMAGE
PROCESSIN
Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection
Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a negative effect on change detection, leading to frequent false alarms in the mapping products. In this research, a novel two-phase object-based deep learning approach is proposed for multi-temporal SAR image change detection. Compared with traditional methods, the proposed approach brings two main innovations. One is to classify all pixels into three categories rather than two categories: unchanged pixels, changed pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation (real changes). The other is to group neighbouring pixels into superpixel objects such as to exploit local spatial context. Two phases are designed in the methodology: (1) Generate objects based on the simple linear iterative clustering (SLIC) algorithm, and discriminate these objects into changed and unchanged classes using fuzzy c-means (FCM) clustering and a deep PCANet. The prediction of this Phase is the set of changed and unchanged superpixels. (2) Deep learning on the pixel sets over the changed superpixels only, obtained in the first phase, to discriminate real changes from false changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained to classify two kinds of changed superpixels to achieve the final change maps. Numerical experiments demonstrate that, compared with benchmark methods, the proposed approach can distinguish real changes from false changes effectively with significantly reduced false alarm rates, and achieve up to 99.71% change detection accuracy using multi-temporal SAR imagery
IE-Net: Information-Enhanced Binary Neural Networks for Accurate Classification
Binary neural networks (BNNs) have been proposed to reduce the heavy memory and computation burdens in deep neural networks. However, the binarized weights and activations in BNNs cause huge information loss, which leads to a severe accuracy decrease, and hinders the real-world applications of BNNs. To solve this problem, in this paper, we propose the information-enhanced network (IE-Net) to improve the performance of BNNs. Firstly, we design an information-enhanced binary convolution (IE-BC), which enriches the information of binary activations and boosts the representational power of the binary convolution. Secondly, we propose an information-enhanced estimator (IEE) to gradually approximate the sign function, which not only reduces the information loss caused by quantization error, but also retains the information of binary weights. Furthermore, by reducing the information loss in binary representations, the novel binary convolution and estimator gain large information compared with the previous work. The experimental results show that the IE-Net achieves accuracies of 88.5% (ResNet-20) and 61.4% (ResNet-18) on CIFAR-10 and ImageNet datasets respectively, which outperforms other SOTA methods. In conclusion, the performance of BNNs could be improved significantly with information enhancement on both weights and activations