Representations and representation learning for image aesthetics prediction and image enhancement

With the continual improvement in cell phone cameras and improvements in the connectivity of mobile devices, we have seen an exponential increase in the images that are captured, stored and shared on social media. For example, as of July 1st 2017 Instagram had over 715 million registered users which had posted just shy of 35 billion images. This represented approximately seven and nine-fold increase in the number of users and photos present on Instagram since 2012. Whether the images are stored on personal computers or reside on social networks (e.g. Instagram, Flickr), the sheer number of images calls for methods to determine various image properties, such as object presence or appeal, for the purpose of automatic image management and curation. One of the central problems in consumer photography centers around determining the aesthetic appeal of an image and motivates us to explore questions related to understanding aesthetic preferences, image enhancement and the possibility of using such models on devices with constrained resources. In this dissertation, we present our work on exploring representations and representation learning approaches for aesthetic inference, composition ranking and its application to image enhancement. Firstly, we discuss early representations that mainly consisted of expert features, and their possibility to enhance Convolutional Neural Networks (CNN). Secondly, we discuss the ability of resource-constrained CNNs, and the different architecture choices (inputs size and layer depth) in solving various aesthetic inference tasks: binary classification, regression, and image cropping. We show that if trained for solving fine-grained aesthetics inference, such models can rival the cropping performance of other aesthetics-based croppers, however they fall short in comparison to models trained for composition ranking. Lastly, we discuss our work on exploring and identifying the design choices in training composition ranking functions, with the goal of using them for image composition enhancement

Learning effective binary representation with deep hashing technique for large-scale multimedia similarity search

The explosive growth of multimedia data in modern times inspires the research of performing an efficient large-scale multimedia similarity search in the existing information retrieval systems. In the past decades, the hashing-based nearest neighbor search methods draw extensive attention in this research field. By representing the original data with compact hash code, it enables the efficient similarity retrieval by only conducting bitwise operation when computing the Hamming distance. Moreover, less memory space is required to process and store the massive amounts of features for the search engines owing to the nature of compact binary code. These advantages make hashing a competitive option in large-scale visual-related retrieval tasks. Motivated by the previous dedicated works, this thesis focuses on learning compact binary representation via hashing techniques for the large-scale multimedia similarity search tasks. Particularly, several novel frameworks are proposed for popular hashing-based applications like a local binary descriptor for patch-level matching (Chapter 3), video-to-video retrieval (Chapter 4) and cross-modality retrieval (Chapter 5). This thesis starts by addressing the problem of learning local binary descriptor for better patch/image matching performance. To this end, we propose a novel local descriptor termed Unsupervised Deep Binary Descriptor (UDBD) for the patch-level matching tasks, which learns the transformation invariant binary descriptor via embedding the original visual data and their transformed sets into a common Hamming space. By imposing a l2,1-norm regularizer on the objective function, the learned binary descriptor gains robustness against noises. Moreover, a weak bit scheme is applied to address the ambiguous matching in the local binary descriptor, where the best match is determined for each query by comparing a series of weak bits between the query instance and the candidates, thus improving the matching performance. Furthermore, Unsupervised Deep Video Hashing (UDVH) is proposed to facilitate large-scale video-to-video retrieval. To tackle the imbalanced distribution issue in the video feature, balanced rotation is developed to identify a proper projection matrix such that the information of each dimension can be balanced in the fixed-bit quantization, thus improving the retrieval performance dramatically with better code quality. To provide comprehensive insights on the proposed rotation, two different video feature learning structures: stacked LSTM units (UDVH-LSTM) and Temporal Segment Network (UDVH-TSN) are presented in Chapter 4. Lastly, we extend the research topic from single-modality to cross-modality retrieval, where Self-Supervised Deep Multimodal Hashing (SSDMH) based on matrix factorization is proposed to learn unified binary code for different modalities directly without the need for relaxation. By minimizing graph regularization loss, it is prone to produce discriminative hash code via preserving the original data structure. Moreover, Binary Gradient Descent (BGD) accelerates the discrete optimization against the bit-by-bit fashion. Besides, an unsupervised version termed Unsupervised Deep Cross-Modal Hashing (UDCMH) is proposed to tackle the large-scale cross-modality retrieval when prior knowledge is unavailable

Superpixel lattices

Superpixels are small image segments that are used in popular approaches to object detection and recognition problems. The superpixel approach is motivated by the observation that pixels within small image segments can usually be attributed the same label. This allows a superpixel representation to produce discriminative features based on data dependent regions of support. The reduced set of image primitives produced by superpixels can also be exploited to improve the efficiency of subsequent processing steps. However, it is common for the superpixel representation to have a different graph structure from the original pixel representation of the image. The first part of the thesis argues that a number of desirable properties of the pixel representation should be maintained by superpixels and that this is not possible with existing methods. We propose a new representation, the superpixel lattice, and demonstrate its advantages. The second part of the thesis investigates incorporating a priori information into superpixel segmentations. We learn a probabilistic model that describes the spatial density of object boundaries in the image. We demonstrate our approach using road scene data and show that our algorithm successfully exploits the spatial distribution of object boundaries to improve the superpixel segmentation. The third part of the thesis presents a globally optimal solution to our superpixel lattice problem in either the horizontal or vertical direction. The solution makes use of a Markov Random Field formulation where the label field is guaranteed to be a set of ordered layers. We introduce an iterative algorithm that uses this framework to learn colour distributions across an image in an unsupervised manner. We conclude that our approach achieves comparable or better performance than competing methods and that it confers several additional advantages

Learning Structured Representations for Understanding Visual and Multimedia Data

Recent advances in Deep Learning (DL) have achieved impressive performance in a variety of Computer Vision (CV) tasks, leading to an exciting wave of academic and industrial efforts to develop Artificial Intelligence (AI) facilities for every aspect of human life. Nevertheless, there are inherent limitations in the understanding ability of DL models, which limit the potential of AI in real-world applications, especially in the face of complex, multimedia input. Despite tremendous progress in solving basic CV tasks, such as object detection and action recognition, state-of-the-art CV models can merely extract a partial summary of visual content, which lacks a comprehensive understanding of what happens in the scene. This is partly due to the oversimplified definition of CV tasks, which often ignore the compositional nature of semantics and scene structure. It is even less studied how to understand the content of multiple modalities, which requires processing visual and textual information in a holistic and coordinated manner, and extracting interconnected structures despite the semantic gap between the two modalities. In this thesis, we argue that a key to improve the understanding capacity of DL models in visual and multimedia domains is to use structured, graph-based representations, to extract and convey semantic information more comprehensively. To this end, we explore a variety of ideas to define more realistic DL tasks in both visual and multimedia domains, and propose novel methods to solve those tasks by addressing several fundamental challenges, such as weak supervision, discovery and incorporation of commonsense knowledge, and scaling up vocabulary. More specifically, inspired by the rich literature of semantic graphs in Natural Language Processing (NLP), we explore innovative scene understanding tasks and methods that describe images using semantic graphs, which reflect the scene structure and interactions between objects. In the first part of this thesis, we present progress towards such graph-based scene understanding solutions, which are more accurate, need less supervision, and have more human-like common sense compared to the state of the art. In the second part of this thesis, we extend our results on graph-based scene understanding to the multimedia domain, by incorporating the recent advances in NLP and CV, and developing a new task and method from the ground up, specialized for joint information extraction in the multimedia domain. We address the inherent semantic gap between visual content and text by creating high-level graph-based representations of images, and developing a multitask learning framework to establish a common, structured semantic space for representing both modalities. In the third part of this thesis, we explore another extension of our scene understanding methodology, to open-vocabulary settings, in order to make scene understanding methods more scalable and versatile. We develop visually grounded language models that use naturally supervised data to learn the meaning of all words, and transfer that knowledge to CV tasks such as object detection with little supervision. Collectively, the proposed solutions and empirical results set a new state of the art for the semantic comprehension of visual and multimedia content in a structured way, in terms of accuracy, efficiency, scalability, and robustness

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data