NDLI: Plant identification with noisy web data

Content Provider	IEEE Xplore Digital Library
Author	Zhang, W.Y. Xian-Sheng Hua
Copyright Year	2014
Abstract	One of the main problems in image based plant identification has been the lack of quality training image data. A few attempts for solving this problem through generating high quality plant images from crowd sourced Web image collections like Flickr are proposed in this paper. These methods try to automatically identify correct and informative training images from those Web images, which typically have very noisy metadata (for example, user tags in Flickr), to enhance existing manually labeled training set. Firstly, for each plant, a set of images is collected from searching Flickr by using the plant name as the query. Then, images are clustered into visually consistent clusters, and in each cluster hopefully a majority of the images are all relevant or irrelevant to the particular plant. From these clusters, a managed plant image data set from ImageCLEF is used as reference to automatically select the highest quality cluster for each plant. The image quality of the selected clusters is further improved by two algorithms: an iterative method and image similarity based ranking. We show that the larger training data set automatically selected by this method significantly increases the accuracy of image based plant identification. In addition, this approach is a generic solution to almost all image recognition problems as long as additional (noisy) training data can be obtained from the Internet automatically.
Starting Page	1
Ending Page	6
File Size	977085
Page Count	6
File Format	PDF
ISBN	9781479947614
DOI	10.1109/ICME.2014.6890180
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Support vector machines Accuracy Training data Noise measurement Data models Clustering algorithms crowd sourced big data Image classification machine learning plant identification
Content Type	Text
Resource Type	Article

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