  In this work, travel destination and business location are taken as venues.
Discovering a venue by a photo is very important for context-aware
applications. Unfortunately, few efforts paid attention to complicated real
images such as venue photos generated by users. Our goal is fine-grained venue
discovery from heterogeneous social multimodal data. To this end, we propose a
novel deep learning model, Category-based Deep Canonical Correlation Analysis
(C-DCCA). Given a photo as input, this model performs (i) exact venue search
(find the venue where the photo was taken), and (ii) group venue search (find
relevant venues with the same category as that of the photo), by the
cross-modal correlation between the input photo and textual description of
venues. In this model, data in different modalities are projected to a same
space via deep networks. Pairwise correlation (between different modal data
from the same venue) for exact venue search and category-based correlation
(between different modal data from different venues with the same category) for
group venue search are jointly optimized. Because a photo cannot fully reflect
rich text description of a venue, the number of photos per venue in the
training phase is increased to capture more aspects of a venue. We build a new
venue-aware multimodal dataset by integrating Wikipedia featured articles and
Foursquare venue photos. Experimental results on this dataset confirm the
feasibility of the proposed method. Moreover, the evaluation over another
publicly available dataset confirms that the proposed method outperforms
state-of-the-arts for cross-modal retrieval between image and text.
