Image Retrieval Based on Interested Objects

ZHANG Feng; ZHONG Bao-jiang

doi:10.3969/j.issn.0372-2112.2018.08.016

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Image Retrieval Based on Interested Objects

更新时间：2025-07-16

- Image Retrieval Based on Interested Objects
- Acta Electronica Sinica Vol. 46, Issue 8, Pages: 1915-1923(2018)
- 作者机构：
  
  苏州大学计算机科学与技术学院,江苏,苏州,215000
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2018.08.016
  CLC： TP391
- Published Online：25 August 2018，
  
  Published：2018
- 稿件说明：
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ZHANG Feng, ZHONG Bao-jiang. Image Retrieval Based on Interested Objects[J]. Acta Electronica Sinica, 2018, 46(8): 1915-1923.
DOI：

ZHANG Feng, ZHONG Bao-jiang. Image Retrieval Based on Interested Objects[J]. Acta Electronica Sinica, 2018, 46(8): 1915-1923. DOI： 10.3969/j.issn.0372-2112.2018.08.016.

摘要

当前图像检索算法通常针对整体图像提取特征以完成检索任务.然而，在很多情况下用户只会关注图像的一部分，即他们的兴趣目标.此时，从整体图像提取的特征一部分是有效的，另一部分则是无效的且会对检索过程带来消极影响.为此，本文提出基于兴趣目标的图像检索方案，并借助于现有的显著性检测、图像分割、特征提取等技术实现一款有效的图像检索算法.首先采用HS （Hierarchical Saliency，分层显著性）检测算法分析用户的兴趣目标并应用SC （Saliency-based Image Cut，基于显著性的图像分割）算法将其分割，然后针对兴趣目标提取HSV （Hue、Saturation、Value，色调、饱和度、明度）颜色特征、SIFT （Scale Invariant Feature Transform，尺度不变特征变换）局部特征和CNN （Convolutional Neural Network，卷积神经网络）语义特征，最后计算其与数据库图像的相似度并根据相似度排序返回检索结果.仿真实验结果表明，本文算法在解决这是什么东西这类图像检索任务时明显优于现有的图像检索算法.

Abstract

The current image retrieval algorithms usually extract features from the whole input image to conduct retrieval tasks.However

in many cases users focus on only a part of the image

i.e.object-of-interest.As a result

the features extracted from the image are partially effective.In other words

some of the features are ineffective and might have a negative impact on the retrieval process.To overcome this difficulty

an image retrieval scheme based on object-of-interest is proposed.By incorporating this retrieval scheme with the existing techniques in saliency detection

image segmentation

and feature extraction

an effective image retrieval algorithm is coded.First

the hierarchical saliency (HS) detection algorithm is adopted to analyze the user's object-of-interest

and the saliency-based image cut (SC) algorithm is employed to segment it from the input image.Then

we extract the hue

saturation

value (HSV) color features

the scale invariant feature transform (SIFT) local features and the convolutional neural network (CNN) semantic features of the object-of-interest.Finally

the similarity of object-of-interest between a query image and every database image is computed and the retrieval result is sorted accordingly.Simulation experimental results show that

when being used to cope with a retrieval task like what is this

the proposed algorithm is significantly better than the current image retrieval algorithms.

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