Xian-Sheng Hua

Bio: Xian-Sheng Hua is an academic researcher from Microsoft. The author has contributed to research in topics: Computer science & TRECVID. The author has an hindex of 52, co-authored 311 publications receiving 8524 citations.

Two-Dimensional Active Learning for image classification

Abstract: In this paper, we propose a two-dimensional active learning scheme and show its application in image classification. Traditional active learning methods select samples only along the sample dimension. While this is the right strategy in binary classification, it is sub-optimal for multi-label classification. In multi-label classification, we argue that, for each selected sample, only a part of more effective labels are necessary to be annotated while others can be inferred by exploring the correlations among the labels. The reason is that the contributions of different labels to minimizing the classification error are different due to the inherent label correlations. To this end, we propose to select sample-label pairs, rather than only samples, to minimize a multi-label Bayesian classification error bound. This new active learning strategy not only considers the sample dimension but also the label dimension, and we call it Two-Dimensional Active Learning (2DAL). We also show that the traditional active learning formulation is a special case of 2DAL when there is only one label. Extensive experiments conducted on two real-world applications show that the 2DAL significantly outperforms the best existing approaches which did not take label correlation into account.

Online video recommendation based on multimodal fusion and relevance feedback

Abstract: With Internet delivery of video content surging to an un-precedented level, video recommendation has become a very popular online service. The capability of recommending relevant videos to targeted users can alleviate users' efforts on finding the most relevant content according to their current viewings or preferences. This paper presents a novel online video recommendation system based on multimodal fusion and relevance feedback. Given an online video document, which usually consists of video content and related information (such as query, title, tags, and surroundings), video recommendation is formulated as finding a list of the most relevant videos in terms of multimodal relevance. We express the multimodal relevance between two video documents as the combination of textual, visual, and aural relevance. Furthermore, since different video documents have different weights of the relevance for three modalities, we adopt relevance feedback to automatically adjust intra-weights within each modality and inter-weights among different modalities by users' click-though data, as well as attention fusion function to fuse multimodal relevance together. Unlike traditional recommenders in which a sufficient collection of users' profiles is assumed available, this proposed system is able to recommend videos without users' profiles. We conducted an extensive experiment on 20 videos searched by top 10 representative queries from more than 13k online videos, reported the effectiveness of our video recommendation system.

Automatic video recommendation

Abstract: Automatic video recommendation is described. The recommendation does not require an existing user profile. The source videos are directly compared to a user selected video to determine relevance, which is then used as a basis for video recommendation. The comparison is performed with respect to a weighted feature set including at least one content-based feature, such as a visual feature, an aural feature and a content-derived textural feature. Multimodal implementation including multimodal features (e.g., visual, aural and textural) extracted from the videos is used for more reliable relevance ranking. One embodiment uses an indirect textural feature generated by automatic text categorization based on a set of predefined category hierarchy. Another embodiment uses self-learning based on user click-through history to improve relevance ranking.

Automatic detection and segmentation of music videos in an audio/video stream

Abstract: A “music video parser” automatically detects and segments music videos in a combined audio-video media stream. Automatic detection and segmentation is achieved by integrating shot boundary detection, video text detection and audio analysis to automatically detect temporal boundaries of each music video in the media stream. In one embodiment, song identification information, such as, for example, a song name, artist name, album name, etc., is automatically extracted from the media stream using video optical character recognition (OCR). This information is then used in alternate embodiments for cataloging, indexing and selecting particular music videos, and in maintaining statistics such as the times particular music videos were played, and the number of times each music video was played.

Real-time large scale near-duplicate web video retrieval

Abstract: Near-duplicate video retrieval is becoming more and more important with the exponential growth of the Web. Though various approaches have been proposed to address this problem, they are mainly focusing on the retrieval accuracy while infeasible to query on Web scale video database in real time. This paper proposes a novel method to address the efficiency and scalability issues for near-duplicate We video retrieval. We introduce a compact spatiotemporal feature to represent videos and construct an efficient data structure to index the feature to achieve real-time retrieving performance. This novel feature leverages relative gray-level intensity distribution within a frame and temporal structure of videos along frame sequence. The new index structure is proposed based on inverted file to allow for fast histogram intersection computation between videos. To demonstrate the effectiveness and efficiency of the proposed methods we evaluate its performance on an open Web video data set containing about 10K videos and compare it with four existing methods in terms of precision and time complexity. We also test our method on a data set containing about 50K videos and 11M key-frames. It takes on average 17ms to execute a query against the whole 50K Web video data set.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Learning from Imbalanced Data

Abstract: With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding of knowledge discovery and analysis from raw data to support decision-making processes. Although existing knowledge discovery and data engineering techniques have shown great success in many real-world applications, the problem of learning from imbalanced data (the imbalanced learning problem) is a relatively new challenge that has attracted growing attention from both academia and industry. The imbalanced learning problem is concerned with the performance of learning algorithms in the presence of underrepresented data and severe class distribution skews. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. In this paper, we provide a comprehensive review of the development of research in learning from imbalanced data. Our focus is to provide a critical review of the nature of the problem, the state-of-the-art technologies, and the current assessment metrics used to evaluate learning performance under the imbalanced learning scenario. Furthermore, in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potential important research directions for learning from imbalanced data.

Active Learning Literature Survey

Abstract: The key idea behind active learning is that a machine learning algorithm can achieve greater accuracy with fewer training labels if it is allowed to choose the data from which it learns. An active learner may pose queries, usually in the form of unlabeled data instances to be labeled by an oracle (e.g., a human annotator). Active learning is well-motivated in many modern machine learning problems, where unlabeled data may be abundant or easily obtained, but labels are difficult, time-consuming, or expensive to obtain. This report provides a general introduction to active learning and a survey of the literature. This includes a discussion of the scenarios in which queries can be formulated, and an overview of the query strategy frameworks proposed in the literature to date. An analysis of the empirical and theoretical evidence for successful active learning, a summary of problem setting variants and practical issues, and a discussion of related topics in machine learning research are also presented.