scholarly journals Weakly Supervised Localization Using Deep Feature Maps

2016 ◽  
pp. 714-731 ◽  
Author(s):  
Archith John Bency ◽  
Heesung Kwon ◽  
Hyungtae Lee ◽  
S. Karthikeyan ◽  
B. S. Manjunath
Keyword(s):  
Feature Maps ◽  
Deep Feature ◽  
Weakly Supervised

scholarly journals High-Resolution SAR Image Classification Using Multi-Scale Deep Feature Fusion and Covariance Pooling Manifold Network

2021 ◽  
Vol 13 (2) ◽  
pp. 328
Author(s):  
Wenkai Liang ◽  
Yan Wu ◽  
Ming Li ◽  
Yice Cao ◽  
Xin Hu
Keyword(s):  
High Resolution ◽  
Image Classification ◽  
Feature Fusion ◽  
Representation Learning ◽  
Sar Image ◽  
Gabor Filtering ◽  
Feature Maps ◽  
Sar Images ◽  
Multi Scale ◽  
Deep Feature

The classification of high-resolution (HR) synthetic aperture radar (SAR) images is of great importance for SAR scene interpretation and application. However, the presence of intricate spatial structural patterns and complex statistical nature makes SAR image classification a challenging task, especially in the case of limited labeled SAR data. This paper proposes a novel HR SAR image classification method, using a multi-scale deep feature fusion network and covariance pooling manifold network (MFFN-CPMN). MFFN-CPMN combines the advantages of local spatial features and global statistical properties and considers the multi-feature information fusion of SAR images in representation learning. First, we propose a Gabor-filtering-based multi-scale feature fusion network (MFFN) to capture the spatial pattern and get the discriminative features of SAR images. The MFFN belongs to a deep convolutional neural network (CNN). To make full use of a large amount of unlabeled data, the weights of each layer of MFFN are optimized by unsupervised denoising dual-sparse encoder. Moreover, the feature fusion strategy in MFFN can effectively exploit the complementary information between different levels and different scales. Second, we utilize a covariance pooling manifold network to extract further the global second-order statistics of SAR images over the fusional feature maps. Finally, the obtained covariance descriptor is more distinct for various land covers. Experimental results on four HR SAR images demonstrate the effectiveness of the proposed method and achieve promising results over other related algorithms.

scholarly journals MICRA-Net: MICRoscopy Analysis Neural Network to solve detection, classification, and segmentation from a single simple auxiliary task

2021 ◽  
Author(s):  
Anthony Bilodeau ◽  
Constantin V.L. Delmas ◽  
Martin Parent ◽  
Paul De Koninck ◽  
Audrey Durand ◽  
...  
Keyword(s):  
Neural Network ◽  
Quantitative Analysis ◽  
High Throughput ◽  
Semantic Segmentation ◽  
Feature Maps ◽  
Expert Annotation ◽  
Microscopy Analysis ◽  
Weakly Supervised ◽  
Segmentation Task ◽  
Microscopy Images

High throughput quantitative analysis of microscopy images presents a challenge due to the complexity of the image content and the difficulty to retrieve precisely annotated datasets. In this paper we introduce a weakly-supervised MICRoscopy Analysis neural network (MICRA-Net) that can be trained on a simple main classification task using image-level annotations to solve multiple the more complex auxiliary semantic segmentation task and other associated tasks such as detection or enumeration. MICRA-Net relies on the latent information embedded within a trained model to achieve performances similar to state-of-the-art fully-supervised learning. This learnt information is extracted from the network using gradient class activation maps, which are combined to generate detailed feature maps of the biological structures of interest. We demonstrate how MICRA-Net significantly alleviates the Expert annotation process on various microscopy datasets and can be used for high-throughput quantitative analysis of microscopy images.

scholarly journals Deep Multiple Instance Hashing for Object-based Image Retrieval

2017 ◽  
Author(s):  
Wanqing Zhao ◽  
Ziyu Guan ◽  
Hangzai Luo ◽  
Jinye Peng ◽  
Jianping Fan
Keyword(s):  
Image Retrieval ◽  
Object Detection ◽  
Multiple Instance Learning ◽  
Feature Maps ◽  
Keyword Query ◽  
The Arts ◽  
Object Based ◽  
Object Proposals ◽  
Weakly Supervised ◽  
Image Pairs

Multi-keyword query is widely supported in text search engines. However, an analogue in image retrieval systems, multi-object query, is rarely studied. Meanwhile, traditional object-based image retrieval methods often involve multiple steps separately and need expensive location labeling for detecting objects. In this work, we propose a weakly-supervised Deep Multiple Instance Hashing (DMIH) framework for object-based image retrieval. DMIH integrates object detection and hashing learning on the basis of a popular CNN model to build the end-to-end relation between a raw image and the binary hashing codes of multiple objects in it. Specifically, we cast the object detection of each object class as a binary multiple instance learning problem where instances are object proposals extracted from multi-scale convolutional feature maps. For hashing training, we sample image pairs to learn their semantic relationships in terms of hash codes of the most probable proposals for owned labels as guided by object predictors. The two objectives benefit each other in learning. DMIH outperforms state-of-the-arts on public benchmarks for object-based image retrieval and achieves promising results for multi-object queries.

scholarly journals Fruit classification using deep feature maps in the presence of deceptive similar classes

2021 ◽  
Author(s):  
Mohit Dandekar ◽  
Narinder Singh Punn ◽  
Sanjay Kumar Sonbhadra ◽  
Sonali Agarwal ◽  
Rage Uday Kiran
Keyword(s):  
Feature Maps ◽  
Deep Feature

A Weakly Supervised Semantic Segmentation Network by Aggregating Seed Cues: The Multi-Object Proposal Generation Perspective

2021 ◽  
Vol 17 (1s) ◽  
pp. 1-19
Author(s):  
Junsheng Xiao ◽  
Huahu Xu ◽  
Honghao Gao ◽  
Minjie Bian ◽  
Yang Li
Keyword(s):  
Image Classification ◽  
Real World ◽  
Semantic Segmentation ◽  
Feature Maps ◽  
High Confidence ◽  
Deep Convolutional Neural Networks ◽  
Object Proposal ◽  
Initial Location ◽  
Weakly Supervised ◽  
High Level

Weakly supervised semantic segmentation under image-level annotations is effectiveness for real-world applications. The small and sparse discriminative regions obtained from an image classification network that are typically used as the important initial location of semantic segmentation also form the bottleneck. Although deep convolutional neural networks (DCNNs) have exhibited promising performances for single-label image classification tasks, images of the real-world usually contain multiple categories, which is still an open problem. So, the problem of obtaining high-confidence discriminative regions from multi-label classification networks remains unsolved. To solve this problem, this article proposes an innovative three-step framework within the perspective of multi-object proposal generation. First, an image is divided into candidate boxes using the object proposal method. The candidate boxes are sent to a single-classification network to obtain the discriminative regions. Second, the discriminative regions are aggregated to obtain a high-confidence seed map. Third, the seed cues grow on the feature maps of high-level semantics produced by a backbone segmentation network. Experiments are carried out on the PASCAL VOC 2012 dataset to verify the effectiveness of our approach, which is shown to outperform other baseline image segmentation methods.

scholarly journals MUSICAL: Multi-Scale Image Contextual Attention Learning for Inpainting

2019 ◽  
Author(s):  
Ning Wang ◽  
Jingyuan Li ◽  
Lefei Zhang ◽  
Bo Du
Keyword(s):  
Image Inpainting ◽  
Superior Performance ◽  
Background Information ◽  
Feature Maps ◽  
Background Region ◽  
Multi Scale ◽  
The Arts ◽  
Deep Feature ◽  
Style Consistency ◽  
Similar Texture

We study the task of image inpainting, where an image with missing region is recovered with plausible context. Recent approaches based on deep neural networks have exhibited potential for producing elegant detail and are able to take advantage of background information, which gives texture information about missing region in the image. These methods often perform pixel/patch level replacement on the deep feature maps of missing region and therefore enable the generated content to have similar texture as background region. However, this kind of replacement is a local strategy and often performs poorly when the background information is misleading. To this end, in this study, we propose to use a multi-scale image contextual attention learning (MUSICAL) strategy that helps to flexibly handle richer background information while avoid to misuse of it. However, such strategy may not promising in generating context of reasonable style. To address this issue, both of the style loss and the perceptual loss are introduced into the proposed method to achieve the style consistency of the generated image. Furthermore, we have also noticed that replacing some of the down sampling layers in the baseline network with the stride 1 dilated convolution layers is beneficial for producing sharper and fine-detailed results. Experiments on the Paris Street View, Places, and CelebA datasets indicate the superior performance of our approach compares to the state-of-the-arts. 

scholarly journals Wildfire Smoke Detection Based on Depthwise Separable Convolutions and Target-Awareness

2020 ◽  
Author(s):  
Yunji Zhao ◽  
Haibo zhang ◽  
Xinliang Zhang ◽  
Wei Qian
Keyword(s):  
Real Time ◽  
Early Warning Systems ◽  
Small Sample ◽  
Complex Environments ◽  
Smoke Detection ◽  
Feature Maps ◽  
Wildfire Smoke ◽  
Deep Feature ◽  
Fire Smoke ◽  
Fire Spreading

Since smoke usually occurs before a flame arises, fire smoke detection is especially significant for early warning systems. In this paper, a DSATA(Depthwise Separability And Target Awareness) algorithm based on depthwise separability and target awareness is proposed. Existing deep learning methods with convolutional neural networks pretrained by abundant and vast datasets are always used to realize generic object recognition tasks. In the area of smoke detection, collecting large quantities of smoke data is a challenging task for small sample smoke objects. The basis is that the objects of interest can be arbitrary object classes with arbitrary forms. Thus, deep feature maps acquired by target-aware pretrained networks are used in modelling these objects of arbitrary forms to distinguish them from unpredictable and complex environments. In this paper, this scheme is introduced to deal with smoke detection. The depthwise separable method with a fixed convolution kernel replacing the training iterations can improve the speed of the algorithm to meet the enhanced requirements of real-time fire spreading for detecting speed. The experimental results demonstrate that the proposed algorithm can detect early smoke, is superior to the state-of-the-art methods in accuracy and speed, and can also realize real-time smoke detection.

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