7037192
Interactive material replacement in photographs
Material replacement has wide application throughout the entertainment industry, particularly for post-production make-up application or wardrobe adjustment. More generally, any low-cost mock-up object can be processed to have the appearance of expensive, high-quality materials. We demonstrate a new system that allows fast, intuitive material replacement in photographs. We extend recent work in object selection and fast texture synthesis, as well as develop a novel approach to shape-from-shading capable of handling objects with albedo changes. Each component of our system runs with interactive speed, allowing for easy experimentation and refinement of results.
14545049
An Axiomatic Approach to IR--UIUC TREC 2005 Robust Track Experiments
In this paper, we report our experiments in the TREC 2005 Robust Track. Our focus is to explore the use of a new axiomatic approach to information retrieval. Most existing retrieval models make the assumption that terms are independent of each other. Although such simplifying assumption has facilitated the construction of successful retrieval systems, the assumption is not true; words are related by use, and their similarity of occurrence in documents can reflect underlying semantic relations between terms. Our new method aims at incorporating term dependency relations into the axiomatic retrieval model in a natural way. In this paper, we describe the method and present analysis of our Robust-2005 evaluation results. The results show that the proposed method works equally well as the KL-divergence retrieval model with a mixture model feedback method. The performance can be further improved by using the external resources such as Google.
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.
12137139
An empirical study of gene synonym query expansion in biomedical information retrieval
Due to the heavy use of gene synonyms in biomedical text, people have tried many query expansion techniques using synonyms in order to improve performance in biomedical information retrieval. However, mixed results have been reported. The main challenge is that it is not trivial to assign appropriate weights to the added gene synonyms in the expanded query; under-weighting of synonyms would not bring much benefit, while overweighting some unreliable synonyms can hurt performance significantly. So far, there has been no systematic evaluation of various synonym query expansion strategies for biomedical text. In this work, we propose two different strategies to extend a standard language modeling approach for gene synonym query expansion and conduct a systematic evaluation of these methods on all the available TREC biomedical text collections for ad hoc document retrieval. Our experiment results show that synonym expansion can significantly improve the retrieval accuracy. However, different query types require different synonym expansion methods, and appropriate weighting of gene names and synonym terms is critical for improving performance.
12874243
A study of methods for negative relevance feedback
Negative relevance feedback is a special case of relevance feedback where we do not have any positive example; this often happens when the topic is difficult and the search results are poor. Although in principle any standard relevance feedback technique can be applied to negative relevance feedback, it may not perform well due to the lack of positive examples. In this paper, we conduct a systematic study of methods for negative relevance feedback. We compare a set of representative negative feedback methods, covering vector-space models and language models, as well as several special heuristics for negative feedback. Evaluating negative feedback methods requires a test set with sufficient difficult topics, but there are not many naturally difficult topics in the existing test collections. We use two sampling strategies to adapt a test collection with easy topics to evaluate negative feedback. Experiment results on several TREC collections show that language model based negative feedback methods are generally more effective than those based on vector-space models, and using multiple negative models is an effective heuristic for negative feedback. Our results also show that it is feasible to adapt test collections with easy topics for evaluating negative feedback methods through sampling.
9077876
Subspace clustering for microarray data analysis:multiple criteria and significance assessment
As one of the latest breakthroughs in experimental molecular biology, microarray technology provides a powerful tool for monitoring the expression patterns of thousands of genes simultaneously, producing huge amounts of valuable gene expression data. Gene expression data are organized as matrices --- tables where rows represent genes, columns represent various samples such as tissues or experimental conditions, and a cell number indicates the expression level of a particular gene in a particular sample.
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.
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.
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.
7764302
Cognitive personal positioning based on activity map and adaptive particle filter
This paper presents a cognitive approach for a reliable yet battery-friendly personal positioning. A user's position is learned from both historical log and possible measurements. Firstly, user's past activities recorded in the log are summarized into an activity map. Accordingly, a user-habit guided particle filtering algorithm is presented for position prediction. Specifically, our algorithm makes reference to the map to determine the most probable correct position, smoothed with occasional measurement. User's current position is modeled probabilistically by a collection of particles and her future moves are modeled with a tendency to follow a familiar path on the map; The estimate is then smoothed by Bayesian filtering. We also allow the number of particles to vary according to user's position in the map. Thus, along with better insights about user's movement experience, our approach can learn from the past and potentially improve the quality of estimates. Our experiments show that this adaptive filtering model using the activity map can deal with non-linear behaviors rather effectively. The new cognitive scheme can indeed track the user's position with a high degree of accuracy. Moreover, the algorithms exhibit low computational complexities, making them well suited for applications on wearable computers.
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.