|START Conference Manager|
ASIST 2012 Annual Meeting
Baltimore, MD, October 26-30, 2012
Image Similarity as Assessed by Users: A Quantitative Study
Pierre Tirilly, Chunsheng Huang, Wooseob Jeong, Xiangming Mu, Iris Xie and Jin Zhang
Image retrieval systems are generally based on the notion of image similarity: they compute similarity scores between the images of the database and a query (image or text), and organize the images according to these scores. However, this notion is ill-defined, and the collections used to train and evaluate image retrieval systems are based on similarity judgments that rely on simplistic, non-realistic, assumptions. This paper addresses the issue of the definition of image similarity, and more precisely the two following questions: do humans assess image similarity in the same way? Is it possible to define reference similarity judgments that would correspond to the perception of most users? An experiment is proposed, in which human subjects are assigned two tasks that fall in principle to the system: rating the similarity of images and ranking images according to a reference image. The data provided by the subjects is analyzed quantitatively to the light of the two aforementioned questions. Results show that the subjects do not have collective strategies of similarity assessment, but that a satisfying consensus can be found individually on the data samples used in the experiments. Based on this, methods to define reference similarity scores and rankings are proposed, that can be used on a larger scale to produce realistic ground truths for the evaluation of image retrieval systems. This study is a first step towards a general, realistic, definition of the notion of image similarity in the context of image retrieval.