Data Driven Approaches for Image & Video Understanding: from Traditional to Zero-shot Supervised Learning

In the present age of advanced computer vision, the necessity of (user-annotated) data is a key factor in image & video understanding. Recent success of deep learning on large scale data has only acted as a catalyst. There are certain problems that exist in this regard: 1) scarcity of (annotated) data, 2) need of expensive manual annotation, 3) problem of change in domain, 4) knowledge base not exhaustive. To make efficient learning systems, one has to be prepared to deal with such diverse set of problems. In terms of data availability, extensive manual annotation can be beneficial in obtaining category specific knowledge. Even then, learning efficient representation for the related task is challenging and requires special attention. On the other hand, when labelled data is scarce, learning category specific representation itself becomes challenging. In this work, I investigate data driven approaches that cater to traditional supervised learning setup as well as an extreme case of data scarcity where no data from test classes are available during training, known as zero-shot learning. First, I look into supervised learning setup with ample annotations and propose efficient dictionary learning technique for better learning of data representation for the task of action classification in images & videos. Then I propose robust mid-level feature representations for action videos that are equally effective in traditional supervised learning as well as zero-shot learning. Finally, I come up with novel approach that cater to zero-shot learning specifically. Thorough discussions followed by experimental validations establish the worth of these novel techniques in solving computer vision related tasks under varying data-dependent scenarios

Mapping Acoustic and Semantic Dimensions of Auditory Perception

Auditory categorisation is a function of sensory perception which allows humans to generalise across many different sounds present in the environment and classify them into behaviourally relevant categories. These categories cover not only the variance of acoustic properties of the signal but also a wide variety of sound sources. However, it is unclear to what extent the acoustic structure of sound is associated with, and conveys, different facets of semantic category information. Whether people use such data and what drives their decisions when both acoustic and semantic information about the sound is available, also remains unknown. To answer these questions, we used the existing methods broadly practised in linguistics, acoustics and cognitive science, and bridged these domains by delineating their shared space. Firstly, we took a model-free exploratory approach to examine the underlying structure and inherent patterns in our dataset. To this end, we ran principal components, clustering and multidimensional scaling analyses. At the same time, we drew sound labels’ semantic space topography based on corpus-based word embeddings vectors. We then built an LDA model predicting class membership and compared the model-free approach and model predictions with the actual taxonomy. Finally, by conducting a series of web-based behavioural experiments, we investigated whether acoustic and semantic topographies relate to perceptual judgements. This analysis pipeline showed that natural sound categories could be successfully predicted based on the acoustic information alone and that perception of natural sound categories has some acoustic grounding. Results from our studies help to recognise the role of physical sound characteristics and their meaning in the process of sound perception and give an invaluable insight into the mechanisms governing the machine-based and human classifications

Investigating human-perceptual properties of "shapes" using 3D shapes and 2D fonts

Shapes are generally used to convey meaning. They are used in video games, films and other multimedia, in diverse ways. 3D shapes may be destined for virtual scenes or represent objects to be constructed in the real-world. Fonts add character to an otherwise plain block of text, allowing the writer to make important points more visually prominent or distinct from other text. They can indicate the structure of a document, at a glance. Rather than studying shapes through traditional geometric shape descriptors, we provide alternative methods to describe and analyse shapes, from a lens of human perception. This is done via the concepts of Schelling Points and Image Specificity. Schelling Points are choices people make when they aim to match with what they expect others to choose but cannot communicate with others to determine an answer. We study whole mesh selections in this setting, where Schelling Meshes are the most frequently selected shapes. The key idea behind image Specificity is that different images evoke different descriptions; but ‘Specific’ images yield more consistent descriptions than others. We apply Specificity to 2D fonts. We show that each concept can be learned and predict them for fonts and 3D shapes, respectively, using a depth image-based convolutional neural network. Results are shown for a range of fonts and 3D shapes and we demonstrate that font Specificity and the Schelling meshes concept are useful for visualisation, clustering, and search applications. Overall, we find that each concept represents similarities between their respective type of shape, even when there are discontinuities between the shape geometries themselves. The ‘context’ of these similarities is in some kind of abstract or subjective meaning which is consistent among different people

Multimodal News Summarization, Tracking and Annotation Incorporating Tensor Analysis of Memes

We demonstrate four novel multimodal methods for efficient video summarization and comprehensive cross-cultural news video understanding. First, For video quick browsing, we demonstrate a multimedia event recounting system. Based on nine people-oriented design principles, it summarizes YouTube-like videos into short visual segments (812sec) and textual words (less than 10 terms). In the 2013 Trecvid Multimedia Event Recounting competition, this system placed first in recognition time efficiency, while remaining above average in description accuracy. Secondly, we demonstrate the summarization of large amounts of online international news videos. In order to understand an international event such as Ebola virus, AirAsia Flight 8501 and Zika virus comprehensively, we present a novel and efficient constrained tensor factorization algorithm that first represents a video archive of multimedia news stories concerning a news event as a sparse tensor of order 4. The dimensions correspond to extracted visual memes, verbal tags, time periods, and cultures. The iterative algorithm approximately but accurately extracts coherent quad-clusters, each of which represents a significant summary of an important independent aspect of the news event. We give examples of quad-clusters extracted from tensors with at least 108 entries derived from international news coverage. We show the method is fast, can be tuned to give preferences to any subset of its four dimensions, and exceeds three existing methods in performance. Thirdly, noting that the co-occurrence of visual memes and tags in our summarization result is sparse, we show how to model cross-cultural visual meme influence based on normalized PageRank, which more accurately captures the rates at which visual memes are reposted in a specified time period in a specified culture. Lastly, we establish the correspondences of videos and text descriptions in different cultures by reliable visual cues, detect culture-specific tags for visual memes and then annotate videos in a cultural settings. Starting with any video with less text or no text in one culture (say, US), we select candidate annotations in the text of another culture (say, China) to annotate US video. Through analyzing the similarity of images annotated by those candidates, we can derive a set of proper tags from the viewpoints of another culture (China). We illustrate cultural-based annotation examples by segments of international news. We evaluate the generated tags by cross-cultural tag frequency, tag precision, and user studies

A DATA DRIVEN APPROACH TO IDENTIFY JOURNALISTIC 5WS FROM TEXT DOCUMENTS

Textual understanding is the process of automatically extracting accurate high-quality information from text. The amount of textual data available from different sources such as news, blogs and social media is growing exponentially. These data encode significant latent information which if extracted accurately can be valuable in a variety of applications such as medical report analyses, news understanding and societal studies. Natural language processing techniques are often employed to develop customized algorithms to extract such latent information from text. Journalistic 5Ws refer to the basic information in news articles that describes an event and include where, when, who, what and why. Extracting them accurately may facilitate better understanding of many social processes including social unrest, human rights violations, propaganda spread, and population migration. Furthermore, the 5Ws information can be combined with socio-economic and demographic data to analyze state and trajectory of these processes. In this thesis, a data driven pipeline has been developed to extract the 5Ws from text using syntactic and semantic cues in the text. First, a classifier is developed to identify articles specifically related to social unrest. The classifier has been trained with a dataset of over 80K news articles. We then use NLP algorithms to generate a set of candidates for the 5Ws. Then, a series of algorithms to extract the 5Ws are developed. These algorithms based on heuristics leverage specific words and parts-of-speech customized for individual Ws to compute their scores. The heuristics are based on the syntactic structure of the document as well as syntactic and semantic representations of individual words and sentences. These scores are then combined and ranked to obtain the best answers to Journalistic 5Ws. The classification accuracy of the algorithms is validated using a manually annotated dataset of news articles