15200693
A formal study of information retrieval heuristics
Empirical studies of information retrieval methods show that good retrieval performance is closely related to the use of various retrieval heuristics, such as TF-IDF weighting. One basic research question is thus what exactly are these "necessary" heuristics that seem to cause good retrieval performance. In this paper, we present a formal study of retrieval heuristics. We formally define a set of basic desirable constraints that any reasonable retrieval function should satisfy, and check these constraints on a variety of representative retrieval functions. We find that none of these retrieval functions satisfies all the constraints unconditionally. Empirical results show that when a constraint is not satisfied, it often indicates non-optimality of the method, and when a constraint is satisfied only for a certain range of parameter values, its performance tends to be poor when the parameter is out of the range. In general, we find that the empirical performance of a retrieval formula is tightly related to how well it satisfies these constraints. Thus the proposed constraints provide a good explanation of many empirical observations and make it possible to evaluate any existing or new retrieval formula analytically.
5670917
Improving the Robustness of Language Models - UIUC TREC 2003 Robust and Genomics Experiments
In this paper, we report our experiments in the TREC 2003 Genomics Track and the Robust Track. A common theme that we explored is the robustness of a basic language modeling retrieval approach. We examine several aspects of robustness, including robustness in handling different types of queries, different types of documents, and optimizing performance for difficult topics. Our basic retrieval method is the KL-divergence retrieval model with the two-stage smoothing method plus a mixture model feedback method. In the Genomics IR track, we propose a new method for modeling semi-structured queries using language models, which is shown to be more robust and effective than the regular query model in handling gene queries. In the Robust track, we experimented with two heuristic approaches to improve the robustness in using language models for pseudo feedback.
16418680
Term feedback for information retrieval with language models
In this paper we study term-based feedback for information retrieval in the language modeling approach. With term feedback a user directly judges the relevance of individual terms without interaction with feedback documents, taking full control of the query expansion process. We propose a cluster-based method for selecting terms to present to the user for judgment, as well as effective algorithms for constructing refined query language models from user term feedback. Our algorithms are shown to bring significant improvement in retrieval accuracy over a non-feedback baseline, and achieve comparable performance to relevance feedback. They are helpful even when there are no relevant documents in the top.
5471705
Probabilistic Models for Expert Finding
A common task in many applications is to find persons who are knowledgeable about a given topic (i.e., expert finding). In this paper, we propose and develop a general probabilistic framework for studying expert finding problem and derive two families of generative models (candidate generation models and topic generation models) from the framework. These models subsume most existing language models proposed for expert finding. We further propose several techniques to improve the estimation of the proposed models, including incorporating topic expansion, using a mixture model to model candidate mentions in the supporting documents, and defining an email count-based prior in the topic generation model. Our experiments show that the proposed estimation strategies are all effective to improve retrieval accuracy.
9299231
Language Models for Expert Finding--UIUC TREC 2006 Enterprise Track Experiments
In this paper, we report our experiments in the TREC 2006 Enterprise Track. Our focus is to study a language model for expert finding. We extend an existing language model for expert retrieval in three aspects. First, we model the document-expert association using a mixure model instead of name matching heuristics as in the existing work; such a mixture model allows us to put different weights on email matching and name matching. Second, we propose to model the prior of an expert based on the counts of email matches in the supporting documents instead of using uniform prior as in the previous work. Finally, we perform topic expansion and generalize the model from computing the likelihood to computing the cross entropy. Our experiments show that the first two extensions are more effective than the third extension, though when optimized, they all seem to be effective.
13897908
Detail preserving shape deformation in image editing
Shape deformation is a common practice in digital image editing, but can unrealistically stretch or compress texture detail. We propose an image editing system that decouples feature position from pixel color generation, by resynthesizing texture from the source image to preserve its detail and orientation around a new feature curve location. We introduce a new distortion to patch-based texture synthesis that aligns texture features with image features. A dense correspondence field between source and target images generated by the control curves then guides texture synthesis.
6933858
Region Growing with Automatic Seeding for Semantic Video Object Segmentation
As content-based multimedia applications become increasingly important, demand for technologies on semantic video object segmentation is growing, where the segmented objects are expected to be in line with human visual perception. Existing research is limited to semi-automatic approach, in which human intervene is often required. These include manual selection of seeds for region growing or manual classification of background edges etc. In this paper, we propose an automatic region growing algorithm for video object segmentation, which features in automatic selection of seeds and thus the entire segmentation does not require any action from human users. Experimental results show that the proposed algorithm performs well in terms of the effectiveness in video object segmentation.
44946141
The Design of an Observer-Based Neural Adaptive Controller for a Class of Nonlinear Systems with Input-Constrained
1454940
Video Indexing and Retrieval in Compressed Domain Using Fuzzy-Categorization
There has been an increased interest in video indexing and retrieval in recent years. In this work, indexing and retrieval system of the visual contents is based on feature extracted from the compressed domain. Direct possessing of the compressed domain spares the decoding time, which is extremely important when indexing large number of multimedia archives. A fuzzy-categorizing structure is designed in this paper to improve the retrieval performance. In our experiment, a database that consists of basketball videos has been constructed for our study. This database includes three categories: full-court match, penalty and close-up. First, spatial and temporal feature extraction is applied to train the fuzzy membership functions using the minimum entropy optimal algorithm. Then, the max composition operation is used to generate a new fuzzy feature to represent the content of the shots. Finally, the fuzzy-based representation becomes the indexing feature for the content-based video retrieval system. The experimental results show that the proposal algorithm is quite promising for semantic-based video retrieval.
1496316
Determination of Total Amino Acids in Oilseed Rape Leaves Using Near Infrared Spectroscopy and Chemometrics
Near infrared spectroscopy combined with chemometrics was investigated to determine the total amino acids (TAA) in oilseed rape leaves. The samples in calibration, validation and prediction set were 80, 40 and 30, respectively. Different spectral preprocessing were compared, and three calibration methods were employed including partial least squares (PLS), multiple linear regression (MLR) and least squares-support vector machine (LS-SVM). The performance evaluation standards were determination coefficients (R2) and root mean square error (RMSE). Successive projections algorithm (SPA) was applied as variable selection method. The optimal model was achieved by SPA-LS-SVM using 13 relevant wavelengths with R2 = 0.9830 and RMSEP = 0.3964. The LS-SVM outperformed PLS and SPA-MLR models. The results indicated that near infrared spectroscopy was successfully applied for the determination of TAA in oilseed rape leaves. This detection method could be used for the on field monitoring of growing status and other physiological parameters of oilseed rape.
8593487
Behavioral Consistency Extraction for Face Verification
According to the preferred embodiment of the present invention pull-up/pull-down circuits are provided that use transistors with different threshold voltages to assure power-up to the correct predetermined state. These circuits have the ability to hold a node up or down while drawing very little DC current. In one embodiment a pull-up/pull-down circuit is provided that powers up to a first state with the pull-up node high and the pull-down node low, and that can be toggled from one state to another. A second embodiment provides a pull-up or pull-down circuit that powers up to the desired state and can be disabled by pulling the pull-up node low or pulling the pull-down node high. The circuits remain disabled until the power to the circuit is cycled.