NDLI: New non-negative sparse feature learning approach for content-based image retrieval

Content Provider	IET Digital Library
Author	Xu, Wangming Wu, Shiqian Er, Meng Joo Zheng, Chaobing Qiu, Yimin
Abstract	One key issue in content-based image retrieval is to extract effective features so as to represent the visual content of an image. In this study, a new non-negative sparse feature learning approach to produce a holistic image representation based on low-level local features is presented. Specifically, a modified spectral clustering method is introduced to learn a non-negative visual dictionary from local features of training images. A non-negative sparse feature encoding method termed non-negative locality-constrained linear coding (NNLLC) is proposed to improve the popular locality-constrained linear coding method so as to obtain more meaningful and interpretable sparse codes for feature representation. Moreover, a new feature pooling strategy named kMaxSum pooling is proposed to alleviate the information loss of the sum pooling or max pooling strategy, which produces a more effective holistic image representation and can be viewed as a generalisation of the sum and max pooling strategies. The retrieval results carried out on two public image databases demonstrate the effectiveness of the proposed approach.
Starting Page	724
Ending Page	733
Page Count	10
ISSN	17519659
Volume Number	11
e-ISSN	17519667
Issue Number	Issue 9, Sep (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/11/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2016.0726
Journal	IET Image Processing
Publisher Date	2017-06-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Content-based Image Retrieval Content-based Retrieval Feature Extraction Feature Pooling Strategy Image Database Image Recognition Image Representation Image Retrieval Information Retrieval Technique KMaxSum Pooling Knowledge Engineering Technique Learning in AI NNLLC Nonnegative Locality-constrained Linear Coding Nonnegative Sparse Feature Learning Approach Spectral Clustering Method
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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