Image Retrieval Based on Similarity Score Fusion from Feature Similarity Ranking Lists

2006 ◽  
pp. 461-470 ◽  
Author(s):  
Mladen Jović ◽  
Yutaka Hatakeyama ◽  
Fangyan Dong ◽  
Kaoru Hirota
Keyword(s):  
Image Retrieval ◽  
Similarity Score ◽  
Feature Similarity ◽  
Score Fusion

scholarly journals Application of Artificial Bee Colony Optimization Algorithm for Image Classification Using Color and Texture Feature Similarity Fusion

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
D. Chandrakala ◽  
S. Sumathi
Keyword(s):  
Image Retrieval ◽  
Optimization Algorithm ◽  
Artificial Bee Colony ◽  
Texture Feature ◽  
Image Database ◽  
Similarity Score ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Very High

With the advancement in image capturing device, the image data is being generated in high volumes. The challenging and important problem in image mining is to reveal useful information by grouping the images into meaningful categories. Image retrieval is extensively required in recent decades because CBIR is regarded as one of the most effective ways of accessing visual data. Conventionally, the way of searching the collections of digital image database is by matching keywords with image caption, descriptions and labels. Keyword based searching method provides very high computational complexity and user has to remember the exact keywords used in the image database. Instead, our paper proposes image retrieval system with Artificial Bee Colony optimization algorithm by fusing similarity score based on color and texture features of an image thereby achieving very high classification accuracy and minimum retrieval time. In this scheme, the color is described by color histogram method in HSV space and texture represented by contrast, energy, entropy, correlation and local stationary over the region in an image. The proposed Comprehensive Image Retrieval scheme fuses the color and texture feature based similarity score between query and all the database images. The experimental results show that the proposed method is superior to keywords based retrieval and content based retrieval schemes with individual low-level features of image.

Commodity Image Retrieval Method Based on CNN and Score Fusion

2021 ◽  
Author(s):  
Zihao Liu ◽  
Xiaoyu Wu ◽  
Jiayao Qian ◽  
Zhiyi Zhu
Keyword(s):  
Image Retrieval ◽  
Retrieval Method ◽  
Score Fusion

scholarly journals Improving Shape Retrieval by Integrating AIR and Modified MutualkNN Graph

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Nouman Qadeer ◽  
Dongting Hu ◽  
Xiabi Liu ◽  
Shahzad Anwar ◽  
Malik Saad Sultan
Keyword(s):  
Image Retrieval ◽  
Shape Matching ◽  
Contextual Information ◽  
Similarity Score ◽  
Feature Representation ◽  
Shape Retrieval ◽  
Computational Time ◽  
Integration Scheme ◽  
Invariant Representation ◽  
Face Datasets

In computer vision, image retrieval remained a significant problem and recent resurgent of image retrieval also relies on other postprocessing methods to improve the accuracy instead of solely relying on good feature representation. Our method addressed the shape retrieval of binary images. This paper proposes a new integration scheme to best utilize feature representation along with contextual information. For feature representation we used articulation invariant representation; dynamic programming is then utilized for better shape matching followed by manifold learning based postprocessing modified mutualkNN graph to further improve the similarity score. We conducted extensive experiments on widely used MPEG-7 database of shape images by so-called bulls-eye score with and without normalization of modified mutualkNN graph which clearly indicates the importance of normalization. Finally, our method demonstrated better results compared to other methods. We also computed the computational time with another graph transduction method which clearly shows that our method is computationally very fast. Furthermore, to show consistency of postprocessing method, we also performed experiments on challenging ORL and YALE face datasets and improved baseline results.

scholarly journals Marker-Less UAV-LiDAR Strip Alignment in Plantation Forests Based on Topological Persistence Analysis of Clustered Canopy Cover

2021 ◽  
Vol 10 (5) ◽  
pp. 284
Author(s):  
Reda Fekry ◽  
Wei Yao ◽  
Lin Cao ◽  
Xin Shen
Keyword(s):  
Score Function ◽  
Canopy Cover ◽  
Tree Height ◽  
Similarity Score ◽  
Rigid Transformation ◽  
Feature Correspondence ◽  
Plantation Forests ◽  
Topological Persistence ◽  
Feature Similarity ◽  
The Individual

A holistic strategy is established for automated UAV-LiDAR strip adjustment for plantation forests, based on hierarchical density-based clustering analysis of the canopy cover. The method involves three key stages: keypoint extraction, feature similarity and correspondence, and rigid transformation estimation. Initially, the HDBSCAN algorithm is used to cluster the scanned canopy cover, and the keypoints are marked using topological persistence analysis of the individual clusters. Afterward, the feature similarity is calculated by considering the linear and angular relationships between each point and the pointset centroid. The one-to-one feature correspondence is retrieved by solving the assignment problem on the similarity score function using the Kuhn–Munkres algorithm, generating a set of matching pairs. Finally, 3D rigid transformation parameters are determined by permutations over all conceivable pair combinations within the correspondences, whereas the best pair combination is that which yields the maximum count of matched points achieving distance residuals within the specified tolerance. Experimental data covering eighteen subtropical forest plots acquired from the GreenValley and Riegl UAV-LiDAR platforms in two scan modes are used to validate the method. The results are extremely promising for redwood and poplar tree species from both the Velodyne and Riegl UAV-LiDAR datasets. The minimal mean distance residuals of 31 cm and 36 cm are achieved for the coniferous and deciduous plots of the Velodyne data, respectively, whereas their corresponding values are 32 cm and 38 cm for the Riegl plots. Moreover, the method achieves both higher matching percentages and lower mean distance residuals by up to 28% and 14 cm, respectively, compared to the baseline method, except in the case of plots with extremely low tree height. Nevertheless, the mean planimetric distance residual achieved by the proposed method is lower by 13 cm.

scholarly journals Searchable Encrypted Image Retrieval Based on Multi-Feature Adaptive Late-Fusion

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 1019
Author(s):  
Wentao Ma ◽  
Jiaohua Qin ◽  
Xuyu Xiang ◽  
Yun Tan ◽  
Zhibin He
Keyword(s):  
Image Retrieval ◽  
Area Under The Curve ◽  
Similarity Score ◽  
Semantic Features ◽  
Cloud Environment ◽  
Late Fusion ◽  
Color Channel ◽  
Encrypted Image ◽  
Single Feature ◽  
Encrypted Images

Recently, searchable encrypted image retrieval in a cloud environment has been widely studied. However, the inappropriate encryption mechanism and single feature description make it hard to achieve the expected effects. Therefore, a major challenge of encrypted image retrieval is how to extract and fuse multiple efficient features to improve performance. Towards this end, this paper proposes a searchable encrypted image retrieval based on multi-feature adaptive late-fusion in a cloud environment. Firstly, the image encryption is completed by designing the encryption function in an RGB color channel, bit plane and pixel position of the image. Secondly, the encrypted images are uploaded to the cloud server and the convolutional neural network (CNN) is fine-tuned to build a semantic feature extractor. Then, low-level features and semantic features are extracted. Finally, the similarity score curves of each feature are calculated, and adaptive late-fusion is performed by the area under the curve. A large number of experiments on public dateset are used to validate the effectiveness of our method.

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