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Compared with supervised learning for feature selection, it is muchmore difficult to select the discriminative features in unsupervised learning due to the lack of label information. Traditional unsupervised feature selection algorithms usually select the features which best preserve the data distribution, e.g., manifold structure, of the whole feature set.(More)
In this paper, we present a new multimedia retrieval paradigm to innovate large-scale search of heterogenous multimedia data. It is able to return results of different media types from heterogeneous data sources, e.g., using a query image to retrieve relevant text documents or images from different data sources. This utilizes the widely available data from(More)
Near-duplicate video retrieval (NDVR) has recently attracted lots of research attention due to the exponential growth of online videos. It helps in many areas, such as copyright protection, video tagging, online video usage monitoring, etc. Most of existing approaches use only a single feature to represent a video for NDVR. However, a single feature is(More)
Most existing cross-modal hashing methods suffer from the scalability issue in the training phase. In this paper, we propose a novel cross-modal hashing approach with a linear time complexity to the training data size, to enable scalable indexing for multimedia search across multiple modals. Taking both the intra-similarity in each modal and the(More)
Near-duplicate video clip (NDVC) detection is an important problem with a wide range of applications such as TV broadcast monitoring, video copyright enforcement, content-based video clustering and annotation, etc. For a large database with tens of thousands of video clips, each with thousands of frames, can NDVC search be performed in real-time? In(More)
Multimedia data are usually represented by multiple features. In this paper, we propose a new algorithm, namely Multifeature Learning via Hierarchical Regression for multimedia semantics understanding, where two issues are considered. First, labeling large amount of training data is labor intensive. It is meaningful to effectively leverage unlabeled data to(More)
With the growing demand for visual information of rich content, effective and efficient manipulations of large video databases are increasingly desired. Many investigations have been made on content-based video retrieval. However, despite the importance, video subsequence identification, which is to find the similar content to a short query clip from a long(More)
The exponential growth of online videos, along with increasing user involvement in video-related activities, has been observed as a constant phenomenon during the last decade. User's time spent on video capturing, editing, uploading, searching, and viewing has boosted to an unprecedented level. The massive publishing and sharing of videos has given rise to(More)
Near-duplicate video retrieval (NDVR) has recently attractedmuch research attention due to the exponential growth of online videos. It has many applications, such as copyright protection, automatic video tagging and online video monitoring. Many existing approaches use only a single feature to represent a video for NDVR. However, a single feature is often(More)
Online video content is surging to an unprecedented level. Massive video publishing and sharing impose heavy demands on online near-duplicate detection for many novel video applications. This paper presents an accurate and practical system for online near-duplicate subsequence detection over continuous video streams. We propose to transform a video stream(More)