Hashing with binary autoencoders

An attractive approach for fast search in image databases is binary hashing, where each high-dimensional, real-valued image is mapped onto a low-dimensional, binary vector and the search is done in this binary space. Finding the optimal hash function is difficult because it involves binary constraints, and most approaches approximate the optimization by relaxing the constraints and then binarizing the result. Here, we focus on the binary autoencoder model, which seeks to reconstruct an image from the binary code produced by the hash function. We show that the optimization can be simplified with the method of auxiliary coordinates. This reformulates the optimization as alternating two easier steps: one that learns the encoder and decoder separately, and one that optimizes the code for each image. Image retrieval experiments, using precision/recall and a measure of code utilization, show the resulting hash function outperforms or is competitive with state-of-the-art methods for binary hashing.Comment: 22 pages, 11 figure

Learning to hash for large scale image retrieval

This thesis is concerned with improving the effectiveness of nearest neighbour search. Nearest neighbour search is the problem of finding the most similar data-points to a query in a database, and is a fundamental operation that has found wide applicability in many fields. In this thesis the focus is placed on hashing-based approximate nearest neighbour search methods that generate similar binary hashcodes for similar data-points. These hashcodes can be used as the indices into the buckets of hashtables for fast search. This work explores how the quality of search can be improved by learning task specific binary hashcodes. The generation of a binary hashcode comprises two main steps carried out sequentially: projection of the image feature vector onto the normal vectors of a set of hyperplanes partitioning the input feature space followed by a quantisation operation that uses a single threshold to binarise the resulting projections to obtain the hashcodes. The degree to which these operations preserve the relative distances between the datapoints in the input feature space has a direct influence on the effectiveness of using the resulting hashcodes for nearest neighbour search. In this thesis I argue that the retrieval effectiveness of existing hashing-based nearest neighbour search methods can be increased by learning the thresholds and hyperplanes based on the distribution of the input data. The first contribution is a model for learning multiple quantisation thresholds. I demonstrate that the best threshold positioning is projection specific and introduce a novel clustering algorithm for threshold optimisation. The second contribution extends this algorithm by learning the optimal allocation of quantisation thresholds per hyperplane. In doing so I argue that some hyperplanes are naturally more effective than others at capturing the distribution of the data and should therefore attract a greater allocation of quantisation thresholds. The third contribution focuses on the complementary problem of learning the hashing hyperplanes. I introduce a multi-step iterative model that, in the first step, regularises the hashcodes over a data-point adjacency graph, which encourages similar data-points to be assigned similar hashcodes. In the second step, binary classifiers are learnt to separate opposing bits with maximum margin. This algorithm is extended to learn hyperplanes that can generate similar hashcodes for similar data-points in two different feature spaces (e.g. text and images). Individually the performance of these algorithms is often superior to competitive baselines. I unify my contributions by demonstrating that learning hyperplanes and thresholds as part of the same model can yield an additive increase in retrieval effectiveness

Learning reliable representations when proxy objectives fail

Representation learning involves using an objective to learn a mapping from data space to a representation space. When the downstream task for which a mapping must be learned is unknown, or is too costly to cast as an objective, we must rely on proxy objectives for learning. In this Thesis I focus on representation learning for images, and address three cases where proxy objectives fail to produce a mapping that performs well on the downstream tasks. When learning neural network mappings from image space to a discrete hash space for fast content-based image retrieval, a proxy objective is needed which captures the requirement for relevant responses to be nearer to the hash of any query than irrelevant ones. At the same time, it is important to ensure an even distribution of image hashes across the whole hash space for efficient information use and high discrimination. Proxy objectives fail when they do not meet these requirements. I propose composing hash codes in two parts. First a standard classifier is used to predict class labels that are converted to a binary representation for state-of-the-art performance on the image retrieval task. Second, a binary deep decision tree layer (DDTL) is used to model further intra-class differences and produce approximately evenly distributed hash codes. The DDTL requires no discretisation during learning and produces hash codes that enable better discrimination between data in the same class when compared to previous methods, while remaining robust to real-world augmentations in the data space. In the scenario where we require a neural network to partition the data into clusters that correspond well with ground-truth labels, a proxy objective is needed to define how these clusters are formed. One such proxy objective involves maximising the mutual information between cluster assignments made by a neural network from multiple views. In this context, views are different augmentations of the same image and the cluster assignments are the representations computed by a neural network. I demonstrate that this proxy objective produces parameters for the neural network that are sub-optimal in that a better set of parameters can be found using the same objective and a different training method. I introduce deep hierarchical object grouping (DHOG) as a method to learn a hierarchy (in the sense of easy-to-hard orderings, not structure) of solutions to the proxy objective and show how this improves performance on the downstream task. When there are features in the training data from which it is easier to compute class predictions (e.g., background colour), when compared to features for which it is relatively more difficult to compute class predictions (e.g., digit type), standard classification objectives (e.g., cross-entropy) fail to produce robust classifiers. The problem is that if a model learns to rely on `easy' features it will also ignore `complex' features (easy versus complex are purely relative in this case). I introduce latent adversarial debiasing (LAD) to decouple easy features from the class labels by first modelling the underlying structure of the training data as a latent representation using a vector-quantised variational autoencoder, and then I use a gradient-based procedure to adjust the features in this representation to confuse the predictions of a constrained classifier trained to predict class labels from the same representation. The adjusted representations of the data are then decoded to produce an augmented training dataset that can be used for training in a standard manner. I show in the aforementioned scenarios that proxy objectives can fail and demonstrate that alternative approaches can mitigate against the associated failures. I suggest an analytic approach to understanding the limits of proxy objectives for every use case in order to make the adjustments to the data or the objectives and ensure good performance on downstream tasks