Region-DH: Region-based Deep Hashing for Multi-Instance Aware Image Retrieval

This paper introduces an instance-aware hashing approach Region-DH for large-scale multi-label image retrieval. The accurate object bounds can significantly increase the hashing performance of instance features. We design a unified deep neural network that simultaneously localizes and recognizes objects while learning the hash functions for binary codes. Region-DH focuses on recognizing objects and building compact binary codes that represent more foreground patterns. Region-DH can flexibly be used with existing deep neural networks or more complex object detectors for image hashing. Extensive experiments are performed on benchmark datasets and show the efficacy and robustness of the proposed Region-DH model

Coupled similarity analysis in supervised learning

University of Technology Sydney. Faculty of Engineering and Information Technology.In supervised learning, the distance or similarity measure is widely used in a lot of classification algorithms. When calculating the categorical data similarity, the strategy used by the traditional classifiers often overlooks the inter-relationship between different data attributes and assumes that they are independent of each other. This can be seen, for example, in the overlap similarity and the frequency based similarity. While for the numerical data, the most used Euclidean distance or Minkowski distance is restricted in each single feature and assumes the features in the dataset have no outer connections. That can cause problems in expressing the real similarity or distance between instances and may give incorrect results if the inter-relationship between attributes is ignored. The same problems exist in other supervised learning, such as the classification tasks of class-imbalance or multi-label. In order to solve these research limitations and challenges, this thesis proposes an insightful analysis on coupled similarity in supervised learning to give an expression of similarity that is more closely related to the real nature of the problem. Firstly, we propose a coupled fuzzy kNN to classify imbalanced categorical data which have strong relationships between objects, attributes and classes in Chapter 3. It incorporates the size membership of a class with attribute weight into a coupled similarity measure, which effectively extracts the intercoupling and intra-coupling relationships from categorical attributes. As it reveals the true inner-relationship between attributes, the similarity strategy we have used can make the instances of each class more compact when measured by the distance. That brings substantial benefits when dealing with class imbalance data. The experiment results show that our supposed method has a more stable and higher average performance than the classic algorithms. We also introduce a coupled similar distance for continuous features, by considering the intra-coupled relationship and inter-coupled relationship between the numerical attributes and their corresponding extensions. As detailed in Chapter 4, we calculate the coupling distance between continuous features based on discrete groups. Substantial experiments have verified that our coupled distance outperforms the original distance, and this is also supported by statistical analysis. When considering the similarity concept, people may only relate to the categorical data, while for the distance concept, people may only take into account the numerical data. Seldom have methods taken into account the both concepts, especially when considering the coupling relationship between features. In Chapter 5, we propose a new method which integrates our coupling concept for mixed type data. In our method, we first do discretization on numerical attributes to transfer such continuous values into separate groups, so as to adopt the inter-coupling distance as we do on categorical features (coupling similarity), then we combine this new coupled distance to the original distance (Euclidean distance), to overcome the shortcoming of the previous algorithms. The experiment results show some improvement when compared to the basic and some variants of kNN algorithms. We also extend our coupling concept to multi-label classification tasks. The traditional single-label classifiers are known to be not suitable for multi-label tasks anymore, owing to the overlap concept of the class labels. The most used classifier in multi-label problems, ML-kNN, learns a single classifier for each label independently, so it is actually a binary relevance classifier. As a consequence, this algorithm is often criticized. To overcome this drawback, we introduce a coupled label similarity, which explores the inner relationship between different labels in multi-label classification according to their natural co-occurrence. This similarity reflects the distance of the different classes. By integrating this similarity with the multi-label kNN algorithm, we improve the performance significantly. Evaluated over three commonly used verification criteria for multi-label classifiers, our proposed coupled multi-label classifier outperforms the ML-kNN, BR-kNN and even IBLR. The result indicates that our supposed coupled label similarity is appropriate for multi-label learning problems and can work more effectively compared to other methods. All the classifiers analyzed in this thesis are based on our coupling similarity (or distance), and applied to different tasks in supervised learning. The performance of these models is examined by widely used verification criteria, such as ROC, Accuracy Rate, Average Precision and Hamming Loss. This thesis provides insightful knowledge for investors to find the inner relationship between features in supervised learning tasks

Learning Models For Corrupted Multi-Dimensional Data: Fundamental Limits And Algorithms

Developing machine learning models for unstructured multi-dimensional datasets such as datasets with unreliable labels and noisy multi-dimensional signals with or without missing information have becoming a central necessity. We are not always fortunate enough to get noise-free datasets for developing classification and representation models. Though there is a number of techniques available to deal with noisy datasets, these methods do not exploit the multi-dimensional structures of the signals, which could be used to improve the overall classification and representation performance of the model. In this thesis, we develop a Kronecker-structure (K-S) subspace model that exploits the multi-dimensional structure of the signal. First, we study the classification performance of K-S subspace models in two asymptotic regimes when the signal dimensions go to infinity and when the noise power tends to zero. We characterize the misclassification probability in terms of diversity order and we drive an exact expression for the diversity order. We further derive a tighter bound on misclassification probability in terms of pairwise geometry of the subspaces. The proposed scheme is optimal in most of the signal dimension regimes except in one regime where the signal dimension is less than twice the subspace dimension, however, hitting such a signal dimension regime is very rare in practice. We empirically show that the classification performance of K-S subspace models agrees with the diversity order analysis. We also develop an algorithm, Kronecker- Structured Learning of Discriminative Dictionaries (K-SLD2), for fast and compact K-S subspace learning for better classification and representation of multidimensional signals. We show that the K-SLD2 algorithm balances compact signal representation and good classification performance on synthetic and real-world datasets. Next, we develop a scheme to detect whether a given multi-dimensional signal with missing information lies on a given K-S subspace. We find that under some mild incoherence conditions we must observe ��(��1 log ��1) number of rows and ��(��2 log ��2) number of columns in order to detect the K-S subspace. In order to account for unreliable labels in datasets we present Nonlinear, Noise- aware, Quasiclustering (NNAQC), a method for learning deep convolutional networks from datasets corrupted by unknown label noise. We append a nonlinear noise model to a standard convolutional network, which is learned in tandem with the parameters of the network. Further, we train the network using a loss function that encourages the clustering of training images. We argue that the non-linear noise model, while not rigorous as a probabilistic model, results in a more effective denoising operator during backpropagation. We evaluate the performance of NNAQC on artificially injected label noise to MNIST, CIFAR-10, CIFAR-100, and ImageNet datasets and on a large-scale Clothing1M dataset with inherent label noise. We show that on all these datasets, NNAQC provides significantly improved classification performance over the state of the art and is robust to the amount of label noise and the training samples