Content-based image retrieval using scale invariant feature transform and gray level co-occurrence matrix

2017 ◽  
Author(s):  
Prashant Srivastava ◽  
Manish Khare ◽  
Ashish Khare
Keyword(s):  
Image Retrieval ◽  
Scale Invariant Feature Transform ◽  
Content Based Image Retrieval ◽  
Gray Level ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Transform ◽  
Occurrence Matrix ◽  
Scale Invariant Feature

scholarly journals Classification of wood knots using artificial neural networks with texture and local feature-based image descriptors

Holzforschung ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sung-Wook Hwang ◽  
Taekyeong Lee ◽  
Hyunbin Kim ◽  
Hyunwoo Chung ◽  
Jong Gyu Choi ◽  
...  
Keyword(s):  
Local Feature ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Based ◽  
Feature Transform ◽  
Artificial Neural ◽  
Occurrence Matrix ◽  
Scale Invariant Feature

Abstract This paper describes feature-based techniques for wood knot classification. For automated classification of macroscopic wood knot images, models were established using artificial neural networks with texture and local feature descriptors, and the performances of feature extraction algorithms were compared. Classification models trained with texture descriptors, gray-level co-occurrence matrix and local binary pattern, achieved better performance than those trained with local feature descriptors, scale-invariant feature transform and dense scale-invariant feature transform. Hence, it was confirmed that wood knot classification was more appropriate for texture classification rather than an approach based on morphological classification. The gray-level co-occurrence matrix produced the highest F1 score despite representing images with relatively low-dimensional feature vectors. The scale-invariant feature transform algorithm could not detect a sufficient number of features from the knot images; hence, the histogram of oriented gradients and dense scale-invariant feature transform algorithms that describe the entire image were better for wood knot classification. The artificial neural network model provided better classification performance than the support vector machine and k-nearest neighbor models, which suggests the suitability of the nonlinear classification model for wood knot classification.

Research and Improvement of Content-Based Image Retrieval Framework

2018 ◽  
Vol 32 (12) ◽  
pp. 1850043 ◽  
Author(s):  
Yong Hou ◽  
Qingjun Wang
Keyword(s):  
Image Retrieval ◽  
High Performance ◽  
Gaussian Mixture ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Invariant Feature ◽  
Match Rate ◽  
Feature Transform ◽  
Scale Invariant Feature

This paper proposed a high-performance image retrieval framework, which combines the improved feature extraction algorithm SIFT (Scale Invariant Feature Transform), improved feature matching, improved feature coding Fisher and improved Gaussian Mixture Model (GMM) for image retrieval. Aiming at the problem of slow convergence of traditional GMM algorithm, an improved GMM is proposed. This algorithm initializes the GMM by using on-line [Formula: see text]-means clustering method, which improves the convergence speed of the algorithm. At the same time, when the model is updated, the storage space is saved through the improvement of the criteria for matching rules and generating new Gaussian distributions. Aiming at the problem that the dimension of SIFT (Scale Invariant Feature Transform) algorithm is too high, the matching speed is too slow and the matching rate is low, an improved SIFT algorithm is proposed, which preserves the advantages of SIFT algorithm in fuzzy, compression, rotation and scaling invariance advantages, and improves the matching speed, the correct match rate is increased by an average of 40% to 55%. Experiments on a recently released VOC 2012 database and a database of 20 category objects containing 230,800 images showed that the framework had high precision and recall rates and less query time. Compared with the standard image retrieval framework, the improved image retrieval framework can detect the moving target quickly and effectively and has better robustness.

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