This paper presents a novel content-based music retrieval system that accepts a query containing multiple tags with multiple levels of preference (denoted as an MTML query) to retrieve music from an untagged music database. We select a limited number of popular music tags to form the tag space and design an interface for users to input queries by operating the scroll bars. To effect MTML content-based music retrieval, we introduce a tag-based music aspect model that jointly models the auditory features and tag-based text features of a song. Two indexing methods and their corresponding matching methods, namely pseudo song-based matching and tag co-occurrence pattern-based matching, are incorporated into the pre-learned tag-based music aspect model. Finally, we evaluate the proposed system on the Major Miner dataset. The results demonstrate the potential of using MTML queries to retrieve music from an untagged music database.

This paper presents a novel content-based query-by-tag music search system for an untagged music database. We design a new tag query interface that allows users to input multiple tags with multiple levels of preference (denoted as an MTML query) by colorizing desired tags in a web-based tag cloud interface. When a user clicks and holds the left mouse button (or presses and holds his/her finger on a touch screen) on a desired tag, the color of the tag will change cyclically according to a color map (from dark blue to bright red), which represents the level of preference (from 0 to 1). In this way, the user can easily organize and check the query of multiple tags with multiple levels of preference through the colored tags. To effect the MTML content-based music retrieval, we introduce a probabilistic fusion model (denoted as GMFM), which consists of two mixture models, namely a Gaussian mixture model and a multinomial mixture model. GMFM can jointly model the auditory features and tag labels of a song. Two indexing methods and their corresponding matching methods, namely pseudo song-based matching and tag affinity-based matching, are incorporated into the pre-learned GMFM. We evaluate the proposed system on the MajorMiner and CAL-500 datasets. The experimental results demonstrate the effectiveness of GMFM and the potential of using MTML queries to search music from an untagged music database.

In real-world information systems, there are abundant unlabeled data but sparse labeled data. It is challenging to construct an adaptive model to classify a large amount of documents containing different domains. The classifiers trained from a source domain shall perform poorly for the test data in a target domain due to the domain mismatch. In this study, we build a topic-bridged latent Dirichlet allocation (TLDA) model from a variety of labeled and unlabeled documents and perform the transfer learning for document classification. The severe change of word distributions is compensated by bridging the latent topics of source and target data which are drawn by the Dirichlet priors. A variational inference procedure is performed for semi-supervised learning. In the experiments on text categorization using 20 Newsgroups dataset, the proposed TLDA model achieved higher classification performance compared to the other methods.

Topic modeling has been widely applied in a variety of text modeling tasks as well as in speech recognition systems for effectively capturing the semantic and statistic information in documents or speech utterances. Most topic models rely on the bag-of-words assumption that results in learned latent topics composed of lists of individual words. Unfortunately, these words may convey topical information but lack accurate semantic knowledge of the text. In this paper, we present the semantic associative topic model, where the concept of the semantic association terms is extended to topic modeling, which provides guidance on modeling the semantic associations that occur among single words by expressing a document as an association of multiple words. Further, the pointwise KL-divergence metric is used to measure the significance of the association. We also integrate original PLSA and SATM models, which have mixed feature representations. Experimental results on WSJ and AP datasets show that the proposed approaches achieved higher performance compared to other methods.

This paper presents a technique for reconstructing a high-quality high dynamic range (HDR) image from a set of differently exposed and possibly blurred images taken with a hand-held camera. Recovering an HDR image from differently exposed photographs has become very popular. However, it often requires a tripod to keep the camera still when taking photographs of different exposures. To ease the process, it is often preferred to use a hand-held camera. This, however, leads to two problems, misaligned photographs and blurred long-exposed photographs. To overcome these problems, this paper adapts an alignment method and proposes a method for HDR reconstruction from possibly blurred images. We use Bayesian framework to formulate the problem and apply a maximum-likelihood approach to iteratively perform blur kernel estimation, HDR image reconstruction and camera curve recovery. When convergence, we simultaneously obtain an HDR image with rich and clear structures, the camera response curve and blur kernels. To show the effectiveness of our method, we test our method on both synthetic and real photographs. The proposed method compares favorably to two other related methods in the experiments.

Statistical language modeling has been successfully developed for speech recognition and information retrieval. The minimum classification error (MCE) training was undertaken to enhance speech recognition performance by minimizing the word error rate. This paper presents a new minimum rank error (MRE) algorithm forn-gram language model training. Rather than speech recognition, the proposed language models are estimated forinformationretrievalby considering the metric ofaverageprecision. However, the maximization of average precision is closely linked to minimizing the rank error or optimizing the order of the ranked documents. Accordingly, this paper calculates therankerrorlossfunctionfrom the misordering pairs of relevant and irrelevant documents in the rank list. The Bayes risk due to the expected rank loss is minimized to develop the Bayesian retrieval rule forad-hocinformation retrieval. Consequently, thediscriminativetrainingof language model is performed by integrating discrimination information from individual relevant documents relative to their corresponding irrelevant documents. Experimental results on TREC collections indicate that the proposed MRE language model improves the order of relevant documents, and degrades that of irrelevant documents. The MRE method achieves significantly higher average precision for test queries than the maximum likelihood and the MCE retrieval models.

Due to the vast growth of data collections, the statistical document modeling has become increasingly important in language processing areas. Probabilistic latent semantic analysis (PLSA) is a popular approach whereby the semantics and statistics can be effectively captured for modeling. However, PLSA is highly sensitive to task domain, which is continuously changing in real-world documents. In this paper, a novel Bayesian PLSA framework is presented. We focus on exploiting the incremental learning algorithm for solving the updating problem of new domain articles. This algorithm is developed to improve document modeling by incrementally extracting up-to-date latent semantic information to match the changing domains at run time. By adequately representing the priors of PLSA parameters using Dirichlet densities, the posterior densities belong to the same distribution so that a reproducible prior/posterior mechanism is activated for incremental learning from constantly accumulated documents. An incremental PLSA algorithm is constructed to accomplish the parameter estimation as well as the hyperparameter updating. Compared to standard PLSA using maximum likelihood estimate, the proposed approach is capable of performing dynamic document indexing and modeling. We also present the maximum a posteriori PLSA for corrective training. Experiments on information retrieval and document categorization demonstrate the superiority of using Bayesian PLSA methods.

Probabilistic latent semantic analysis (PLSA) is a popular approach to text modeling where the semantics and statistics in documents can be effectively captured. In this paper, a novel Bayesian PLSA framework is presented. We focus on exploiting the incremental learning algorithm for solving the updating problem of new domain articles. This algorithm is developed to improve text modeling by incrementally extracting the up-to-date latent semantic information to match the changing domains at run time. The expectationmaximization (EM) algorithm is applied to resolve the quasiBayes (QB) estimate of PLSA parameters. The online PLSA is constructed to accomplish parameter estimation as well as hyperparameter updating. Compared to standard PLSA using maximum likelihood estimate, the proposed QB approach is capable of performing dynamic document indexing and classification. Also, we present the maximum a posteriori PLSA for corrective training. Experiments on evaluating model perplexities and classification accuracies demonstrate the superiority of using Bayesian PLSA.

Language modeling plays a critical role for automatic speech recognition. Typically, the n-gram language models suffer from the lack of a good representation of historical words and an inability to estimate unseen parameters due to insufficient training data. In this study, we explore the application of latent semantic information (LSI) to language modeling and parameter smoothing. Our approach adopts latent semantic analysis to transform all words and documents into a common semantic space. The word-to-word, word-to-document and document-to-document relations are, accordingly, exploited for language modeling and smoothing. For language modeling, we present a new representation of historical words based on retrieval of the most relevant document. We also develop a novel parameter smoothing method, where the language models of seen and unseen words are estimated by interpolating the k nearest seen words in the training corpus. The interpolation coefficients are determined according to the closeness of words in the semantic space. As shown by experiments, the proposed modeling and smoothing methods can significantly reduce the perplexity of language models with moderate computational cost.