Video Saliency Detection by using an Enhance Methodology Involving a Combination of 3DCNN with Histograms

When watching pictures or videos, the Human Visual System has the potential to concentrate on important locations. Saliency detection is a tool for detecting the abnormality and randomness of images or videos by replicating the human visual system. Video saliency detection has received a lot of attention in recent decades, but due to challenging temporal abstraction and fusion for spatial saliency, computational modelling of spatial perception for video sequences is still limited.Unlike methods for detection of salient objects in still images, one of the most difficult aspects of video saliency detection is figuring out how to isolate and integrate spatial and temporal features.Saliency detection, which is basically a tool to recognize areas in images and videos that catch the attention of the human visual system, may benefit multimedia applications such as video or image retrieval, copy detection, and so on. As the two crucial steps in trajectory-based video classification methods are feature point identification and local feature extraction. We suggest a new spatio-temporal saliency detection using an enhanced 3D Conventional neural network with an inclusion of histogram for optical and orient gradient in this paper

Hyperpixels: Flexible 4D over-segmentation for dense and sparse light fields

4D Light Field (LF) imaging, since it conveys both spatial and angular scene information, can facilitate computer vision tasks and generate immersive experiences for end-users. A key challenge in 4D LF imaging is to flexibly and adaptively represent the included spatio-angular information to facilitate subsequent computer vision applications. Recently, image over-segmentation into homogenous regions with perceptually meaningful information has been exploited to represent 4D LFs. However, existing methods assume densely sampled LFs and do not adequately deal with sparse LFs with large occlusions. Furthermore, the spatio-angular LF cues are not fully exploited in the existing methods. In this paper, the concept of hyperpixels is defined and a flexible, automatic, and adaptive representation for both dense and sparse 4D LFs is proposed. Initially, disparity maps are estimated for all views to enhance over-segmentation accuracy and consistency. Afterwards, a modified weighted K-means clustering using robust spatio-angular features is performed in 4D Euclidean space. Experimental results on several dense and sparse 4D LF datasets show competitive and outperforming performance in terms of over-segmentation accuracy, shape regularity and view consistency against state-of-the-art methods.info:eu-repo/semantics/publishedVersio

Remote Sensing Scene Classification Based on Convolutional Neural Networks Pre-Trained Using Attention-Guided Sparse Filters

Open access articleSemantic-level land-use scene classification is a challenging problem, in which deep learning methods, e.g., convolutional neural networks (CNNs), have shown remarkable capacity. However, a lack of sufficient labeled images has proved a hindrance to increasing the land-use scene classification accuracy of CNNs. Aiming at this problem, this paper proposes a CNN pre-training method under the guidance of a human visual attention mechanism. Specifically, a computational visual attention model is used to automatically extract salient regions in unlabeled images. Then, sparse filters are adopted to learn features from these salient regions, with the learnt parameters used to initialize the convolutional layers of the CNN. Finally, the CNN is further fine-tuned on labeled images. Experiments are performed on the UCMerced and AID datasets, which show that when combined with a demonstrative CNN, our method can achieve 2.24% higher accuracy than a plain CNN and can obtain an overall accuracy of 92.43% when combined with AlexNet. The results indicate that the proposed method can effectively improve CNN performance using easy-to-access unlabeled images and thus will enhance the performance of land-use scene classification especially when a large-scale labeled dataset is unavailable

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin