Research Projects

Social Event Recognition from Static Images

We propose to leverage concept-level representations for complex event recognition in photographs given limited training examples. We introduce a novel framework to discover event concept attributes from the web and use that to extract semantic features from images and classify them into social event categories with few training examples. Discovered concepts include a variety of objects, scenes, actions and event sub-types, leading to a discriminative and compact representation for event images. Web images are obtained for each discovered event concept and we use (pretrained) CNN features to train concept classifiers. Extensive experiments on challenging event datasets demonstrate that our proposed method outperforms several baselines using deep CNN features directly in classifying images into events with limited training examples. We also demonstrate that our method achieves the best overall accuracy on a dataset with unseen event categories using a single training example. [paper][project]

Towards Using Visual Attributes to Infer Image Sentiment Of Social Events

Widespread and pervasive adoption of smartphones has led to instant sharing of photographs that capture events ranging from mundane to life-altering happenings. We propose to capture sentiment information of such social event images leveraging their visual content. Our method extracts an intermediate visual representation of social event images based on the visual attributes that occur in the images going beyond
sentiment-specific attributes. We map the top predicted attributes to sentiments and extract the dominant emotion associated with a picture of a social event. Unlike recent approaches, our method generalizes to a variety of social events and even to unseen events, which are not available at training time. We demonstrate the effectiveness of our approach on a challenging social event image dataset and our method outperforms state-of-the-art approaches for classifying complex event images into sentiments. [paper]

Social Event Detection Using Kernel Canonical Correlation Analysis

Sharing user experiences in the form of photographs, tweets, text, audio and/or video has become commonplace in social media. Browsing through uploaded content of a particular event remains cumbersome. It requires a user to initiate textual search query and manually go through a list of resulting images to find relevant information. We propose an automatic clustering algorithm, which given a large collection of images, groups them into a cluster of different events using the image features and the related metadata. We formulate this problem as a kernel canonical correlation clustering problem in which data samples from different modalities or ‘views’ are projected to a space where correlations between the samples’ projections are maximized. [poster]