Object-based image retrieval with kernel on adjacency matrix and local combined features

Author:

Qi Heng1,Li Keqiu1,Shen Yanming1,Qu Wenyu2

Affiliation:

1. Dalian University of Technology, Dalian, China

2. Dalian Maritime University, Dalian, China

Abstract

In object-based image retrieval, there are two important issues: an effective image representation method for representing image content and an effective image classification method for processing user feedback to find more images containing the user-desired object categories. In the image representation method, the local-based representation is the best selection for object-based image retrieval. As a kernel-based classification method, Support Vector Machine (SVM) has shown impressive performance on image classification. But SVM cannot work on the local-based representation unless there is an appropriate kernel. To address this problem, some representative kernels are proposed in literatures. However, these kernels cannot work effectively in object-based image retrieval due to ignoring the spatial context and the combination of local features. In this article, we present Adjacent Matrix (AM) and the Local Combined Features (LCF) to incorporate the spatial context and the combination of local features into the kernel. We propose the AM-LCF feature vector to represent image content and the AM-LCF kernel to measure the similarities between AM-LCF feature vectors. According to the detailed analysis, we show that the proposed kernel can overcome the deficiencies of existing kernels. Moreover, we evaluate the proposed kernel through experiments of object-based image retrieval on two public image sets. The experimental results show that the performance of object-based image retrieval can be improved by the proposed kernel.

Funder

National Natural Science Foundation of China

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture

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1. Robust Semi‐nonnegative Matrix Factorization with Adaptive Graph Regularization for Gene Representation;Chinese Journal of Electronics;2020-01

2. Approximate Nearest Neighbor Search Based on Hierarchical Multi-Index Hashing;2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI);2018-10

3. Robust Semisupervised Nonnegative Local Coordinate Factorization for Data Representation;Complexity;2018-08-01

4. Robust Local Learning and Discriminative Concept Factorization for Data Representation;IEEE Access;2018

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