Driven by the needs of customers and industry, online fashion search and analytics are recently gaining much attention. As fashion is mostly expressed by visual content, the analysis of fashion images in online social networks is a rich source of possible insights on evolving trends and customer preferences. Although a plethora of visual content is available, the modeling of clothes physics and movement, the implicit semantics in the fashion designs and the subjectivity of their interpretation pose difficulties to fully automated solutions for fashion search and analysis. In this paper we present the design and evaluation of a crowd-powered system for fashion similarity search from Twitter, supporting trend analysis for fashion professionals. The system enables fashion similarity search based on specific human-based similarity criteria. This is achieved by implementing a novel machine-crowd workflow that supports complex tasks requiring highly subjective judgments where multiple true solutions may co-exist. We discuss how this leads to a novel class of crowdpowered systems where the output of the crowd is not used to verify the automatic analysis but is the desired outcome. Finally, we show how such kind of crowd involvement enables a novel kind of similarity search and represents a crucial factor for the acceptance of system results by the end-user.