Abstract
This paper proposes an improved method for extracting NMI features. This method uses Particle Swarm Optimization in advance to optimize the two-dimensional maximum class-to-class variance (2OTSU) in advance. Afterwards, the optimized 2OUSU is introduced into the Pulse Coupled Neural Network (PCNN) to automatically obtain the number of iterations of the loop. We use an improved PCNN method to extract the NMI features of the image. For the problem of low accuracy of single feature, this paper proposes a new method of multi-feature fusion based on image retrieval. It uses HSV colour features and texture features, where, the texture feature extraction methods include: Grey Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Improved PCNN. The experimental results show that: on the Corel-1k dataset, compared with similar algorithms, the retrieval accuracy of this method is improved by 13.6%; On the AT&T dataset, the retrieval accuracy is improved by 13.4% compared with the similar algorithm; on the FD-XJ dataset, the retrieval accuracy is improved by 17.7% compared with the similar algorithm. Therefore, the proposed algorithm has better retrieval performance and robustness compared with the existing image retrieval algorithms based on multi-feature fusion.
Highlights
Image retrieval has received more and more attention in recent years
This paper fused the features extracted by HSV, Grey Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) and the NMI feature extracted by the improved Pulse Coupled Neural Network (PCNN) to improve the retrieval accuracy
This paper presents a novel multi-feature fusion image retrieval method
Summary
Image retrieval has received more and more attention in recent years. It refers to the process of searching for a query image in a database, and searching for and representing images related to user needs [1]. The early text-based image retrieval algorithm is a method for manually annotating image content and performing keyword search. The main research was on image retrieval based on single features. Q Liu et al [9] proposed an image NMI feature extraction based on pulsecoupled neural network This method avoids poor image retrieval results caused by image smoothing and compression, the number of loop iterations needs to be determined artificially when extracting NMI features, resulting in the accuracy has declined. This paper fused the features extracted by HSV, GLCM and LBP and the NMI feature extracted by the improved PCNN to improve the retrieval accuracy. The rest of the paper is structured as follows: Section 2 reviews the related work of image retrieval; Section 3 describes proposed method; Section 4 explicates experimental results and analysis; Section 5 summarizes this paper
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