Towards Evaluating and Simulating Keyword Queries for Development of Interactive Known-item Search Systems
Abstract
Searching for memorized images in large datasets (known-item search) is a challenging task due to a limited effectiveness of retrieval models as well as limited ability of users to formulate suitable queries and choose an appropriate search strategy. A popular option to approach the task is to automatically detect semantic concepts and rely on interactive specification of keywords during the search session.
Nonetheless, employed instances of such search models are often set arbitrarily in existing KIS systems as comprehensive evaluations with reals users are time demanding. This paper envisions and investigates an option to simulate keyword queries in a selected "toy'' (yet competitive) keyword search model relying on a deep image classification network.
Specifically, two properties of such keyword-based model are experimentally investigated with our known-item search benchmark dataset: which output transformation and ranking models are effective for the utilized classification model and whether there are some options for simulations of keyword queries. In addition to the main objective, the paper inspects also the effect of interactive query reformulations for the considered keyword search model.