Multimodal fusion using learned text concepts for image categorization
Conventional image categorization techniques primarily rely on low-level visual cues. In this paper, we describe a multimodal fusion scheme which improves the image classification accuracy by incorporating the information derived from the embedded texts detected in the image under classification. Specific to each image category, a text concept is first learned from a set of labeled texts in images of the target category using Multiple Instance Learning . For an image under classification which contains multiple detected text lines, we calculate a weighted Euclidian distance between each text line and the learned text concept of the target category. Subsequently, the minimum distance, along with low-level visual cues, are jointly used as the features for SVM-based classification. Experiments on a challenging image database demonstrate that the proposed fusion framework achieves a higher accuracy than the state-of-art methods for image classification.

title: Using visual features for anti-spam filtering
abstract: Unsolicited commercial email (UCE), also known as spam, has been a major problem on the Internet. In the past, researchers have addressed this problem as a text classification or categorization problem. However, as spammers' techniques continue to evolve and the genre of email content becomes more and more diverse, text-based anti-spam approaches alone are no longer sufficient. In this paper, we propose a novel anti-spam system which utilizes visual clues, in addition to text information in the email body, to determine whether a message is spam. We analyze a large collection of spam emails containing images and identify a number of useful visual features for this application. We then propose using one-class support vector machines (SVM) as the underlying base classifier for anti-spam filtering. The experimental results demonstrate that the proposed system can add significant filtering power to the existing text-based anti-spam filters.

title: Adaptive learning of an accurate skin-color model
abstract: Due to variations of lighting conditions, camera hardware settings, and the range of skin coloration among human beings, a pre-defined skin-color model cannot accurately capture the wide distribution of skin colors in individual images. In this paper, we propose an adaptive skin-detection method, which allows modeling true skin-color distribution with significantly higher accuracy and flexibility than other methods attain. In principle, the proposed method follows a two-step process. For a given image, we first perform a rough skin classification using a generic skin model, which defines the skin-similar space. In the second step, a Gaussian mixture model (GMM), specific to the image under consideration and refined from the skin-similar space, is derived using the standard expectation-maximization (EM) algorithm. Then, we use an SVM (support vector machine) classifier to identify the skin Gaussian from the trained GMM (which contains two Gaussian components) by incorporating spatial and shape information of the skin pixels. This adaptive method can be applied to both still images and video applications. Results of extensive experiments performed on live video sequences and large image databases have demonstrated the effectiveness and benefits of the proposed model.

title: A unified adaptive approach to accurate skin detection
abstract: Due to variations of lighting conditions and camera hardware settings and the existence of many ethnic people with a wide range of skin colors, a generic skin model is often inadequate to accurately capture the skin distribution for individual images. In this paper, we propose an adaptive skin detection framework, which allows modeling title skin distribution with significantly higher accuracy and flexibility. First, an adaptive skin model, specific to the image under consideration and refined from the skin-similar space, is derived using a Gaussian mixture model (GMM) and standard expectation maximization (EM) algorithm. Then, we develop a support vector machine (SVM) classifier to identify the skin Gaussian from the trained GMM (with two Gaussian components) by incorporating spatial and shape information of skin pixels. Extensive experimental results performed on large image databases have demonstrated the effectiveness and benefits of the proposed approach.

title: Hierarchical model based human motion tracking
abstract: Image sequence based tracking is the pivotal technique of human motion. In the paper, we first propose an appropriate human model and color model. Second, two approaches are proposed aiming at two different levels of color-block information including the boundary and the inner-area: the extraction of the boundary algorithm is based on the Robert operator and the clustering algorithm is based on self-adaptation. Third, we unite two regions, which are processed by different approaches. This step counteracts the ambiguity of obtaining information from each level. Finally, after the rough area of the color-block is obtained, the boundary of the color-block is determined by calculating the histogram of the X,Y coordinate of every point on the boundary of the block. Experimental results are presented.

title: Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
abstract: We integrate the cascade-of-rejectors approach with the Histograms of Oriented Gradients (HoG) features to achieve a fast and accurate human detection system. The features used in our system are HoGs of variable-size blocks that capture salient features of humans automatically. Using AdaBoost for feature selection, we identify the appropriate set of blocks, from a large set of possible blocks. In our system, we use the integral image representation and a rejection cascade which significantly speed up the computation. For a 320 × 280 image, the system can process 5 to 30 frames per second depending on the density in which we scan the image, while maintaining an accuracy level similar to existing methods.

title: A Fuzzy Query Optimization Approach for Multidatabase Systems
abstract: A crucial challenge for global query optimization in a multidatabase system (MDBS) is that some local optimization information, such as local cost parameters, may not be accurately known at the global level because of local autonomy. Traditional query optimization techniques using a crisp cost model may not be suitable for an MDBS because precise information is required. In this paper we present a new approach that performs global query optimization using a fuzzy cost model that allows fuzzy information. We suggest methods for establishing a fuzzy cost model and introduce a fuzzy optimization criterion that can be used with a fuzzy cost model. We discuss the relationship between the fuzzy optimization approach and the traditional (crisp) optimization approach and show that the former has a better chance to find a good execution strategy for a query in an MDBS environment, but its complexity may grow exponentially compared with the complexity of the later. To reduce the complexity, we suggest to use so-called k-approximate fuzzy values to approximate all fuzzy values during fuzzy query optimization. It is proven that the improved fuzzy approach has the same order of complexity as the crisp approach.

title: On k-Nearest Neighbor Searching in Non-Ordered Discrete Data Spaces
abstract: A k-nearest neighbor (k-NN) query retrieves k objects from a database that are considered to be the closest to a given query point. Numerous techniques have been proposed in the past for supporting efficient k-NN searches in continuous data spaces. No such work has been reported in the literature for k-NN searches in a non-ordered discrete data space (NDDS). Performing k-NN searches in an NDDS raises new challenges. The Hamming distance is usually used to measure the distance between two vectors (objects) in an NDDS. Due to the coarse granularity of the Hamming distance, a k-NN query in an NDDS may lead to a large set of candidate solutions, creating a high degree of non-determinism for the query result. We propose a new distance measure, called granularity-enhanced Hamming (GEH) distance, that effectively reduces the number of candidate solutions for a query. We have also considered using multidimensional database indexing for implementing k-NN searches in NDDSs. Our experiments on synthetic and genomic data sets demonstrate that our index-based k-NN algorithm is effective and efficient in finding k-NNs in NDDSs.

title: A piggyback method to collect statistics for query optimization in database management systems
abstract: A database management system (DBMS) performs query optimization based on statistical information about data in the underlying data-base. Out-of-date statistics may lead to inefficient query processing in the system. Existing solutions to this problem have some drawbacks such as heavy administrative burden, high system load, and tardy updates. To overcome these drawbacks, our new approach, called the piggyback method, is proposed in this paper. The key idea is to piggyback some additional retrievals during the processing of a user query in order to collect more up-to-date statistics. The collected statistics are used to optimize the processing of subsequent queries. To specify the piggybacked queries, basic piggybacking operators are defined in this paper. Using the operators, several types of piggybacking such as vertical, horizontal, mixed vertical and horizontal, and multi-query piggybacking are introduced. Statistics that can be obtained from different access methods by applying piggyback analysis during query processing are also studied. In order to meet users' different requirements for the associated overhead, several piggybacking levels are suggested. Other related issues including initial statistics, piggybacking time, and parallelism are also discussed. Our analysis shows that the piggyback method is promising in improving the quality of query optimization in a DBMS as well as in reducing the user's administrative burden for maintaining an efficient DBMS.

title: Evolutionary techniques for updating query cost models in a dynamic multidatabase environment
abstract: Deriving local cost models for query optimization in a dynamic multidatabase system (MDBS) is a challenging issue. In this paper, we study how to evolve a query cost model to capture a slowly-changing dynamic MDBS environment so that the cost model is kept up-to-date all the time. Two novel evolutionary techniques, i.e., the shifting method and the block-moving method, are proposed. The former updates a cost model by taking up-to-date information from a new sample query into consideration at each step, while the latter considers a block (batch) of new sample queries at each step. The relevant issues, including derivation of recurrence updating formulas, development of efficient algorithms, analysis and comparison of complexities, and design of an integrated scheme to apply the two methods adaptively, are studied. Our theoretical and experimental results demonstrate that the proposed techniques are quite promising in maintaining accurate cost models efficiently for a slowly changing dynamic MDBS environment. Besides the application to MDBSs, the proposed techniques can also be applied to the automatic maintenance of cost models in self-managing database systems.

title: The Hybrid Digital Tree: A New Indexing Technique for Large String Databases
abstract: There is an increasing demand for efficient indexing techniques to support queries on large string databases. In this paper, a hybrid RAM/disk-based index structure, called the Hybrid Digital tree (HD-tree), is proposed. The HD-tree keeps internal nodes in the RAM to minimize the number of disk I/Os, while maintaining leaf nodes on the disk to maximize the capability of the tree for indexing large databases. Experimental results using real data have shown that the HD-tree outperformed the Prefix B-tree for prefix and substring searches. In particular, for distinctive random queries in the experiments, the average number of disk I/Os was reduced by a factor of two to three, while the running time was reduced in an order of magnitude.