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1. QUANTITATIVE EVALUATION OF A LOW-COST NONINVASIVE HYBRID
INTERFACE BASED ON EEG AND EYE MOVEMENT
ABSTRACT
Current image search engines suffer from a radicalvariance in retrieval performance over
different queries. It istherefore desirable to identify those “difficult” queries in orderto handle
them properly. Query difficulty estimation is an attemptto predict the performance of the search
results returnedby an image search system. Most existing methods for querydifficulty estimation
focus on investigating statistical characteristicsof the returned images only, while neglecting very
importantinformation, i.e., the query and its relationship with returnedimages. This relationship
plays a crucial role in query difficultyestimation and should be explored further. In this paper
wepropose a novel query difficulty estimation method with queryreconstruction error. This
method is proposed based on the observationthat, given the images returned for an unknown
query, wecan easily deduce what the query is from those images if the searchresults are high
quality (i.e., lots of relevant images returned);otherwise, it is difficult to deduce the original
query. Therefore,we propose to predict the query difficulty by measuring to whatextent the
original query can be recovered from the image searchresults. Specifically, we first reconstruct a
visual query from thereturned images to summarize their visual theme, and then usethe
reconstruction error, i.e., the distance between the originaltextual query and the reconstructed
visual query, to estimatethe query difficulty. We conduct extensive experiments on tworeal-
world Web image datasets and demonstrate the effectivenessof the proposed method.