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 maximumlikelihood 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.
Abstract. Text classication (TC) has long been an important research topic in information retrieval (IR) related areas. In the literature, the bag-of-words (BoW) model has been widely used to represent a document in text classication and many other applications. However, BoW, which ignores the relationships between terms, oers a rather poor document representation. Some previous research has shown that incorporating language models into the naive Bayes classier (NBC) can improve the performance of text classication. Although the widely used N -gram language models (LM) can exploit the relationships between words to some extent, they cannot model the long-distance dependencies of words. In this paper, we study the term association modeling approach within the translation LM framework for TC. The new model is called the term association translation model (TATM). The innovation is to incorporate term associations into the document model. We employ the term translation model to model such associative terms in the documents. The term association translation model can be learned based on either the joint probability (JP) of the associative terms through the Bayes rule or the mutual information (MI) of the associative terms. The results of TC experiments evaluated on the Reuters-21578 and 20newsgroups corpora demonstrate that the new model implemented in both ways outperforms the standard NBC method and the NBC with a unigram LM.
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.Tag cloud-based music query interface, MTML query, contentbased music information retrieval, probabilistic fusion model.
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