Business-Aware Visual Concept Discovery from Social Media for Multimodal Business Venue Recognition

Abstract

Image localization is important for marketing and recommendation of local business; however, the level of granularity is still a critical issue. Given a consumer photo and its rough GPS information, we are interested in extracting the fine-grained location information (i.e. business venues) of the image. To this end, we propose a novel framework for business venue recognition. The framework mainly contains three parts. First, business aware visual concept discovery: we mine a set of concepts that are useful for business venue recognition based on three guidelines including business-awareness, visually detectable, and discriminative power. Second, business-aware concept detection by convolutional neural networks (BA-CNN): we pro- pose a new network architecture that can extract semantic concept features from input image. Third, multimodal business venue recognition: we extend visually detected concepts to multimodal feature representations that allow a test image to be associated with business reviews and images from social media for business venue recognition. The experiments results show the visual concepts detected by BA-CNN can achieve up to 22.5% relative improvement for business venue recognition compared to the state-of-the-art convolutional neural network features. Experiments also show that by leveraging multimodal information from social media we can further boost the performance, especially in the case when the database images belonging to each business venue are scarce.