Combination of Local Feature Extraction for Image Retrieval

2022 ◽  
pp. 319-328
Author(s):  
S. Sankara Narayanan ◽  
D. Vinod ◽  
Suganya Athisayamani ◽  
A. Robert Singh
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Local Feature ◽  
Local Feature Extraction

scholarly journals Object-based Image Retrieval using Local Feature Extraction and Relevance Feedback

2013 ◽  
Vol 78 (7) ◽  
pp. 8-14
Author(s):  
Mário H.G.Freitas ◽  
Flávio L. C. Pádua ◽  
Guilherme T. Assis
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Relevance Feedback ◽  
Local Feature ◽  
Object Based ◽  
Local Feature Extraction

scholarly journals A FLEXIBLE SUB-BLOCK IN REGION BASED IMAGE RETRIEVAL BASED ON TRANSITION REGION

2018 ◽  
Vol 11 (1) ◽  
pp. 42
Author(s):  
Ahmad Wahyu Rosyadi ◽  
Renest Danardono ◽  
Siprianus Septian Manek ◽  
Agus Zainal Arifin
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Transition Region ◽  
Local Binary Patterns ◽  
Local Feature ◽  
Global Features ◽  
Query Image ◽  
Block Based ◽  
Hsv Histogram ◽  
Local Feature Extraction

One of the techniques in region based image retrieval (RBIR) is comparing the global feature of an entire image and the local feature of image’s sub-block in query and database image. The determined sub-block must be able to detect an object with varying sizes and locations. So the sub-block with flexible size and location is needed. We propose a new method for local feature extraction by determining the flexible size and location of sub-block based on the transition region in region based image retrieval. Global features of both query and database image are extracted using invariant moment. Local features in database and query image are extracted using hue, saturation, and value (HSV) histogram and local binary patterns (LBP). There are several steps to extract the local feature of sub-block in the query image. First, preprocessing is conducted to get the transition region, then the flexible sub-block is determined based on the transition region. Afterward, the local feature of sub-block is extracted. The result of this application is the retrieved images ordered by the most similar to the query image. The local feature extraction with the proposed method is effective for image retrieval with precision and recall value are 57%.

Content-based image retrieval via a hierarchical-local-feature extraction scheme

2018 ◽  
Vol 77 (21) ◽  
pp. 29099-29117 ◽  
Author(s):  
Muwei Jian ◽  
Yilong Yin ◽  
Junyu Dong ◽  
Kin-Man Lam
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Local Feature ◽  
Content Based Image Retrieval ◽  
Extraction Scheme ◽  
Local Feature Extraction

Novel local feature extraction for age invariant face recognition

2021 ◽  
Vol 175 ◽  
pp. 114786
Author(s):  
Rajesh Kumar Tripathi ◽  
Anand Singh Jalal
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Local Feature ◽  
Local Feature Extraction

scholarly journals Classification of Textures Using Filter Based Local Feature Extraction

2016 ◽  
Vol 75 ◽  
pp. 03001
Author(s):  
Veysel Gokhan Bocekci ◽  
Kazim Yildiz
Keyword(s):  
Feature Extraction ◽  
Local Feature ◽  
Local Feature Extraction

scholarly journals Memory-Augmented Transformer for Remote Sensing Image Semantic Segmentation

2021 ◽  
Vol 13 (22) ◽  
pp. 4518
Author(s):  
Xin Zhao ◽  
Jiayi Guo ◽  
Yueting Zhang ◽  
Yirong Wu
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Semantic Segmentation ◽  
Local Features ◽  
Local Feature ◽  
Global Information ◽  
Deep Convolutional Neural Networks ◽  
Global Representation ◽  
Local Feature Extraction ◽  
Global And Local

The semantic segmentation of remote sensing images requires distinguishing local regions of different classes and exploiting a uniform global representation of the same-class instances. Such requirements make it necessary for the segmentation methods to extract discriminative local features between different classes and to explore representative features for all instances of a given class. While common deep convolutional neural networks (DCNNs) can effectively focus on local features, they are limited by their receptive field to obtain consistent global information. In this paper, we propose a memory-augmented transformer (MAT) to effectively model both the local and global information. The feature extraction pipeline of the MAT is split into a memory-based global relationship guidance module and a local feature extraction module. The local feature extraction module mainly consists of a transformer, which is used to extract features from the input images. The global relationship guidance module maintains a memory bank for the consistent encoding of the global information. Global guidance is performed by memory interaction. Bidirectional information flow between the global and local branches is conducted by a memory-query module, as well as a memory-update module, respectively. Experiment results on the ISPRS Potsdam and ISPRS Vaihingen datasets demonstrated that our method can perform competitively with state-of-the-art methods.

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