Instagram is a rich source for mining descriptive tags for images and multimedia in general. The tags– image pairs can be used to train automatic image annotation (AIA) systems in accordance with the learning by example paradigm. In previous studies, we had concluded that, on average, 20% of the Instagram hash tags are related to the actual visual content of the image they accompany, i.e., they are descriptive hash tags, while there are many irrelevant hash tags, i.e., stop-hash tags, that are used across totally different images just for gathering clicks and for search ability enhancement. In this paper, we present a novel methodology, based on the principles of collective intelligence that helps in locating those hash tags. In particular, we show that the application of a modified version of the well-known hyperlink induced topic search (HITS) algorithm, in a crowd tagging context, provides an effective and consistent way for finding pairs of Instagram images and hash tags, which lead to representative and noise-free training sets for content-based image retrieval. As a proof of concept, we used the crowd sourcing platform Figure-eight to allow collective intelligence to be gathered in the form of tag selection (crowd tagging) for Instagram hash tags. The crowd tagging data of Figure-eight are used to form bipartite graphs in which the first type of nodes corresponds to the annotators and the second type to the hash tags they selected. The HITS algorithm is first used to rank the annotators in terms of their effectiveness in the crowd tagging task and then to identify the right hash tags per image.

HITS, Hash tags, CNN, ML