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Image retrieval based on AND/OR-construction models

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Abstract

With the rapid development of the Internet, finding desired images from numerous images has become an important research topic. In this paper, we propose an image retrieval system facilitating retrieval time and accuracy. Since the performance of image retrieval is deeply influenced by image features and retrieval methods. Five different types of features and five different methods are used to find the best combination for an image retrieval system. First, we segment out the main object in an image and then extract its features. Next, relevant features are selected from the original feature set for facilitating image retrieval, using the SAHS algorithm. Then, five methods based on AND/OR-construction are proposed to build the image retrieval model, using the relevant features. Finally, the experimental results not only show that our methods are more effective than the other state-of-the-art methods but also present some observations never explored by the previous research.

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Abbreviations

(SAHS):

Self-Adaptive Harmony Search

(DNN):

Deep Neural Networks

(LSH):

Locality-Sensitive Hashing

(SH):

Spectral Hashing

(AGH):

Anchor Graph Hashing

(IMH):

Inductive Manifold Hashing

(ITQ):

Iterative Quantization

(SP):

Sparse Projection

(AIBC):

Asymmetric Inner-product Binary Coding

(DSTH):

Discrete Semantic Transfer Hashing

(MLH):

Minimal Loss Hashing

(KSH):

Kernel-based Supervised Hashing

(RSH):

Ranking-based Supervised Hashing

(SDH):

Supervised Discrete Hashing

(DPLM):

Discrete Proximal Linearized Minimization

(DDQH):

Discriminative Deep Quantization Hashing

(SSDH):

Semi-Supervised Discrete Hashing

(DBSCAN):

Density-based Spatial Clustering of Applications with Noise

(MPEG):

Moving Picture Experts Group

(CBIR):

Content-based Information Retrieval

(ACM):

Active Contour Model

(SFM):

Sparse Field Method

(ICC):

Intraclass Correlation Coefficient

(LBFGS):

Limited-memory Broyden-Fletcher-Goldfarb-Shanno

(MAP):

Mean Average Precision

(AP):

Average Precision

(CCA-ITQ):

Canonical Correlation Analysis Iterative Quantization

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, 123 University Road, Section 3, Touliu, Yunlin, 640, Taiwan, Republic of China

    Yin-Fu Huang & Yun-Shin Hsieh

Authors
  1. Yin-Fu Huang
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  2. Yun-Shin Hsieh
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Contributions

YF proposed the methods and analyzed the experimental results of image retrieval. YS implemented the platform and conducted a series of experiments on an Intel Core 3.60GHz, 48GB RAM with Windows 10. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yin-Fu Huang.

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Availability of data and material

The datasets analyzed during the current study are available from CIFAR-10, NUS-WIDE, and ILSVRC-2012.

Competing interests

The authors declare that they have no competing interests

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Huang, YF., Hsieh, YS. Image retrieval based on AND/OR-construction models. Multimed Tools Appl 79, 27293–27320 (2020). https://doi.org/10.1007/s11042-020-09274-x

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