2373278
A discriminative HMM/N-gram-based retrieval approach for mandarin spoken documents
In recent years, statistical modeling approaches have steadily gained in popularity in the field of information retrieval. This article presents an HMM/N-gram-based retrieval approach for Mandarin spoken documents. The underlying characteristics and the various structures of this approach were extensively investigated and analyzed. The retrieval capabilities were verified by tests with word- and syllable-level indexing features and comparisons to the conventional vector-space model approach. To further improve the discrimination capabilities of the HMMs, both the expectation-maximization (EM) and minimum classification error (MCE) training algorithms were introduced in training. Fusion of information via indexing word- and syllable-level features was also investigated. The spoken document retrieval experiments were performed on the Topic Detection and Tracking Corpora (TDT-2 and TDT-3). Very encouraging retrieval performance was obtained.
7426756
Content-based language models for spoken document retrieval
Spoken document retrieval (SDR) has been extensively studied in recent years because of its potential use in navigating large multimedia collections in the near future. This paper presents a novel concept of applying the content-based language models to spoken document retrieval. In an example task for retrieval of Mandarin broadcast news, the content-based language models either trained with the automatic transcriptions of the spoken documents or adapted from the baseline language models using the automatic transcriptions of the spoken documents were used to create the more accurate recognition results and indexing terms from both the spoken documents and the speech queries. We report on some interesting findings obtained in this research.
38323853
Content-based Language Models for Spoken Document Retrieval
Spoken document retrieval (SDR) has been extensively studied in recent years because of its potential use in navigating large multimedia collections in the near future. This paper presents a novel concept of applying content-based language models to spoken document retrieval. In an example task for retrieval of Mandarin Chinese broadcast news data, the content-based language models, either trained on automatic transcriptions of spoken documents or adapted from baseline language models using automatic transcriptions of spoken documents, were used to create more accurate recognition results and indexing terms from both spoken documents and speech queries. We report on some interesting findings obtained in this research.
15709443
Voice retrieval of Mandarin broadcast news speech
This paper presents an improved framework for voice retrieval of Mandarin broadcast news speech. First, several unsupervised and data-driven approaches for broadcast news transcription were proposed to improve the speech recognition accuracy and efficiency. Then, a multiscale indexing paradigm for broadcast news retrieval was exploited to alleviate the problems caused by the speech recognition errors and the flexible wording structure of the Chinese language. Finally, we used the PDA as the platform and broadcast radio programs collected in Taiwan as the document collection to establish a speech-based multimedia information retrieval prototype system. Very encouraging results were obtained.
10957724
Footprint area sampled texturing
We study texture projection based on a four region subdivision: magnification, minification, and two mixed regions. We propose improved versions of existing techniques by providing exact filtering methods which reduce both aliasing and overblurring, especially in the mixed regions. We further present a novel texture mapping algorithm called FAST (footprint area sampled texturing), which not only delivers high quality, but also is efficient. By utilizing coherence between neighboring pixels, performing prefiltering, and applying an area sampling scheme, we guarantee a minimum number of samples sufficient for effective antialiasing. Unlike existing methods (e.g., MlP-map, Feline), our method adapts the sampling rate in each chosen MlP-map level separately to avoid undersampling in the lower level l for effective antialiasing and to avoid oversampling in the higher level l+1 for efficiency. Our method has been shown to deliver superior image quality to Feline and other methods while retaining the same efficiency. We also provide implementation trade offs to apply a variable degree of accuracy versus speed.
2577652
Bellwether analysis: predicting global aggregates from local regions
Massive datasets are becoming commonplace in a wide range of domains, and mining them is recognized as a challenging problem with great potential value. Motivated by this challenge, much effort has been concentrated on developing scalable versions of machine learning algorithms. An often overlooked issue is that large datasets are rarely labeled with the outputs that we wish to learn to predict, due to the human labor required. We make the key observation that analysts can often use queries to define labels for cases, which leads to the problem of learning to predict such query-produced labels. Of course, if a dataset is available in its entirety, we can simply run the query again to compute labels. The interesting scenarios are those where, after the predictive model is trained, new data is gathered at significant incremental cost and, perhaps, over time. The challenge is to accurately predict the query-labels for the projected completion of new datasets, based only on certain cost-effective subsets, which we call bellwethers.
4025640
Algorithms for on-line bin-packing problems with cardinality constraints
The bin-packing problem asks for a packing of a list of items of sizes from (0,1] into the smallest possible number of bins having unit capacity. The k-item bin-packing problem additionally imposes the constraint that at most k items are allowed in one bin. We present two efficient on-line algorithms for this problem. We show that, for increasing values of k, the bound on the asymptotic worst-case performance ratio of the first algorithm tends towards 2 and that the second algorithm has a ratio of 2. Both algorithms considerably improve upon the best known result of an algorithm, which has an asymptotic bound of 2.7 on its ratio. Moreover, we improve known bounds for all values of k by presenting on-line algorithms for k = 2 and 3 with bounds on their ratios close to optimal.
16773627
Illustrating surfaces in volume
This paper presents a novel framework for illustrating surfaces in a volume. Surfaces are illustrated by drawing only feature lines, such as silhouettes, valleys, ridges, and surface hatching strokes, and are embedded in volume renderings. This framework promises effective illustration of both surfaces and volumes without occluding or cluttering each other. A two-step approach has been taken: the first step depicts surfaces; the second step performs volume rendering, at the same time embedding surfaces from the first step.
 We introduce Procedurally Perturbed Image Processing (PIP), a new method for enhancing both feature detection and depiction of surfaces. We also present implementation strategies, especially those leveraging modern graphics hardware, for delivering an interactive rendering system. Our implementation results have shown that this mixed form of rendering improves volume visualization and is efficient.
1524685
Achievable performance of digital watermarking systems
A variety of digital watermarking applications have emerged recently that require the design of systems for embedding one signal (the "embedded signal" or "watermark") within another signal (the "host signal"). We develop a framework for analyzing achievable performance trade-offs of these systems among robustness, distortion, and embedding rate. We also describe a recently introduced class of embedding methods, quantization index modulation (QIM), in which an ensemble of quantizers is constructed and information is embedded by quantizing the host signal with a quantizer associated with the watermark. We introduce an implementation of such a method called spread-transform dither modulation where the embedded information modulates the dither signal of a dithered quantizer, which quantizes projections of the host signal onto a spreading vector. We show that QIM systems have considerable performance advantages over previously proposed spread-spectrum and low-bit modulation systems.