Incremental hashing with sample selection using dominant sets

In the world of big data, large amounts of images are available in social media, corporate and even personal collections. A collection may grow quickly as new images are generated at high rates. The new images may cause changes in the distribution of existing classes or the emergence of new classes, resulting in the collection being dynamic and having concept drift. For efficient image retrieval from an image collection using a query, a hash table consisting of a set of hash functions is needed to transform images into binaryhash codeswhich are used as the basis to find similar images to the query. If the image collection is dynamic, the hash table built at one time step may not work well at the next due to changes in the collection as a result of new images being added. Therefore, the hash table needs to be rebuilt or updated at successive time steps. Incremental hashing (ICH) is the first effective method to deal with the concept drift problem in image retrieval from dynamic collections. In ICH, a new hash table is learned based on newly emerging images only which represent data distribution of the current data environment. The new hash table is used to generate hash codes for all images including old and new ones. Due to the dynamic nature, new images of one class may not be similar to old images of the same class. In order to learn new hash table that preserves within-class similarity in both old and new images,incremental hashing with sample selection using dominant sets(ICHDS) is proposed in this paper, which selects representative samples from each class for training the new hash table. Experimental results show that ICHDS yields better retrieval performance than existing dynamic and static hashing methods

Event Detection from Social Media Stream: Methods, Datasets and Opportunities

Social media streams contain large and diverse amount of information, ranging from daily-life stories to the latest global and local events and news. Twitter, especially, allows a fast spread of events happening real time, and enables individuals and organizations to stay informed of the events happening now. Event detection from social media data poses different challenges from traditional text and is a research area that has attracted much attention in recent years. In this paper, we survey a wide range of event detection methods for Twitter data stream, helping readers understand the recent development in this area. We present the datasets available to the public. Furthermore, a few research opportunitiesComment: 8 page

Dynamic match kernel with deep convolutional features for image retrieval

For image retrieval methods based on bag of visual words, much attention has been paid to enhancing the discriminative powers of the local features. Although retrieved images are usually similar to a query in minutiae, they may be significantly different from a semantic perspective, which can be effectively distinguished by convolutional neural networks (CNN). Such images should not be considered as relevant pairs. To tackle this problem, we propose to construct a dynamic match kernel by adaptively calculating the matching thresholds between query and candidate images based on the pairwise distance among deep CNN features. In contrast to the typical static match kernel which is independent to the global appearance of retrieved images, the dynamic one leverages the semantical similarity as a constraint for determining the matches. Accordingly, we propose a semantic-constrained retrieval framework by incorporating the dynamic match kernel, which focuses on matched patches between relevant images and filters out the ones for irrelevant pairs. Furthermore, we demonstrate that the proposed kernel complements recent methods, such as hamming embedding, multiple assignment, local descriptors aggregation, and graph-based re-ranking, while it outperforms the static one under various settings on off-the-shelf evaluation metrics. We also propose to evaluate the matched patches both quantitatively and qualitatively. Extensive experiments on five benchmark data sets and large-scale distractors validate the merits of the proposed method against the state-of-the-art methods for image retrieval

Incremental learning algorithms and applications

International audienceIncremental learning refers to learning from streaming data, which arrive over time, with limited memory resources and, ideally, without sacrificing model accuracy. This setting fits different application scenarios where lifelong learning is relevant, e.g. due to changing environments , and it offers an elegant scheme for big data processing by means of its sequential treatment. In this contribution, we formalise the concept of incremental learning, we discuss particular challenges which arise in this setting, and we give an overview about popular approaches, its theoretical foundations, and applications which emerged in the last years

Online hashing for fast similarity search

In this thesis, the problem of online adaptive hashing for fast similarity search is studied. Similarity search is a central problem in many computer vision applications. The ever-growing size of available data collections and the increasing usage of high-dimensional representations in describing data have increased the computational cost of performing similarity search, requiring search strategies that can explore such collections in an efficient and effective manner. One promising family of approaches is based on hashing, in which the goal is to map the data into the Hamming space where fast search mechanisms exist, while preserving the original neighborhood structure of the data. We first present a novel online hashing algorithm in which the hash mapping is updated in an iterative manner with streaming data. Being online, our method is amenable to variations of the data. Moreover, our formulation is orders of magnitude faster to train than state-of-the-art hashing solutions. Secondly, we propose an online supervised hashing framework in which the goal is to map data associated with similar labels to nearby binary representations. For this purpose, we utilize Error Correcting Output Codes (ECOCs) and consider an online boosting formulation in learning the hash mapping. Our formulation does not require any prior assumptions on the label space and is well-suited for expanding datasets that have new label inclusions. We also introduce a flexible framework that allows us to reduce hash table entry updates. This is critical, especially when frequent updates may occur as the hash table grows larger and larger. Thirdly, we propose a novel mutual information measure to efficiently infer the quality of a hash mapping and retrieval performance. This measure has lower complexity than standard retrieval metrics. With this measure, we first address a key challenge in online hashing that has often been ignored: the binary representations of the data must be recomputed to keep pace with updates to the hash mapping. Based on our novel mutual information measure, we propose an efficient quality measure for hash functions, and use it to determine when to update the hash table. Next, we show that this mutual information criterion can be used as an objective in learning hash functions, using gradient-based optimization. Experiments on image retrieval benchmarks confirm the effectiveness of our formulation, both in reducing hash table recomputations and in learning high-quality hash functions

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE