Residual Enhanced Image Descriptor

2019 ◽  
Vol 31 (6) ◽  
pp. 1039
Author(s):  
Benchang Wei ◽  
Li Zheng ◽  
Tao Guan
Keyword(s):  
Image Descriptor

scholarly journals Unsupervised Deep Feature Learning for Remote Sensing Image Retrieval

2018 ◽  
Vol 10 (8) ◽  
pp. 1243 ◽  
Author(s):  
Xu Tang ◽  
Xiangrong Zhang ◽  
Fang Liu ◽  
Licheng Jiao
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Feature Learning ◽  
Remote Sensing Image ◽  
Code Word ◽  
Image Features ◽  
Learning Method ◽  
L1 Norm ◽  
Image Descriptor ◽  
Image Archives

Due to the specific characteristics and complicated contents of remote sensing (RS) images, remote sensing image retrieval (RSIR) is always an open and tough research topic in the RS community. There are two basic blocks in RSIR, including feature learning and similarity matching. In this paper, we focus on developing an effective feature learning method for RSIR. With the help of the deep learning technique, the proposed feature learning method is designed under the bag-of-words (BOW) paradigm. Thus, we name the obtained feature deep BOW (DBOW). The learning process consists of two parts, including image descriptor learning and feature construction. First, to explore the complex contents within the RS image, we extract the image descriptor in the image patch level rather than the whole image. In addition, instead of using the handcrafted feature to describe the patches, we propose the deep convolutional auto-encoder (DCAE) model to deeply learn the discriminative descriptor for the RS image. Second, the k-means algorithm is selected to generate the codebook using the obtained deep descriptors. Then, the final histogrammic DBOW features are acquired by counting the frequency of the single code word. When we get the DBOW features from the RS images, the similarities between RS images are measured using L1-norm distance. Then, the retrieval results can be acquired according to the similarity order. The encouraging experimental results counted on four public RS image archives demonstrate that our DBOW feature is effective for the RSIR task. Compared with the existing RS image features, our DBOW can achieve improved behavior on RSIR.

A new Rényi entropy-based local image descriptor for object recognition

2010 ◽  
Author(s):  
S. Gabarda ◽  
G. Cristóbal ◽  
P. Rodríguez ◽  
C. Miravet ◽  
J. M. del Cura
Keyword(s):  
Object Recognition ◽  
Renyi Entropy ◽  
Rényi Entropy ◽  
Image Descriptor ◽  
Local Image Descriptor ◽  
Local Image

A local image descriptor based on radial and angular gradient intensity histogram for blurred image matching

2018 ◽  
Vol 35 (10) ◽  
pp. 1373-1391 ◽  
Author(s):  
Bahman Sadeghi ◽  
Kamal Jamshidi ◽  
Abbas Vafaei ◽  
S. Amirhassan Monadjemi
Keyword(s):  
Image Matching ◽  
Image Descriptor ◽  
Intensity Histogram ◽  
Blurred Image ◽  
Local Image Descriptor ◽  
Local Image

scholarly journals 3D Human Motion Tracking and Reconstruction Using DCT Matrix Descriptor

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Alireza Behrad ◽  
Nadia Roodsarabi
Keyword(s):  
3D Reconstruction ◽  
Discrete Cosine Transform ◽  
Motion Tracking ◽  
Image Sequence ◽  
Human Motion ◽  
Human Motion Analysis ◽  
Image Descriptor ◽  
Tracking Process ◽  
Position And Orientation ◽  
Proper Frequency

One of the most important issues in human motion analysis is the tracking and 3D reconstruction of human motion, which utilizes the anatomic points' positions. These points can uniquely define the position and orientation of all anatomical segments. In this work, a new method is proposed for tracking and 3D reconstruction of human motion from the image sequence of a monocular static camera. In this method, 2D tracking is used for 3D reconstruction, which a database of selected frames is used for the correction of tracking process. The method utilizes a new image descriptor based on discrete cosine transform (DCT), which is employed in different stages of the algorithm. The advantage of using this descriptor is the capabilities of selecting proper frequency regions in various tasks, which results in an efficient tracking and pose matching algorithms. The tracking and matching algorithms are based on reference descriptor matrixes (RDMs), which are updated after each stage based on the frequency regions in DCT blocks. Finally, 3D reconstruction is performed using Taylor’s method. Experimental results show the promise of the algorithm.

Sign in / Sign up

Export Citation Format

Share Document