27646240
Mining User Position Log for Construction of Personalized Activity Map
Consider a scenario in which a smart phone automatically saves the user's positional records for personalized location-based applications. The smart phone will infer patterns of user activities from the historical records and predict user's future movements. In this paper, we present algorithms for mining the evolving positional logs in order to identify places of significance to user and representative paths connecting these places, based on which a personalized activity map is constructed. In addition, the map is designed to contain information of speed and transition probabilities, which are used in predicting the user's future movements. Our experiments show that the user activity map well matches the actual traces and works effectively in predicting user's movements.
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.
8868065
Controlling Uncertainty in Personal Positioning at Minimal Measurement Cost
One interesting scenario in personal positioning involves an energy-conscious mobile user who tries to obtain estimates about his positions with sufficiently high confidence while consuming as little battery energy as possible. Besides obtaining estimates directly from a position measuring device, the user can rely on extrapolative calculations based on a user movement model and a known initial estimate. Because each measuring probe usually incurs a substantially higher cost than the extrapolative calculation, the objective is to minimize the overall cost of the measurement probes. 
 
Assuming that the user moves at a normally-distributed velocity, we consider two scenarios which differ in the probing devices used. In the first scenario, only one probing device is used. In this case, the aim is to minimize the total number of probes required. In the second scenario, two types of positioning devices are given, where one type of devices offers a higher positioning precision, but also at a greater probing cost. In this case, the aim is to choose an optimal combination of probes from the two types of devices. 
 
For both scenarios, we present algorithms for determining the minimum-cost probing sequences. The algorithms are computationally efficient in reducing the searching space of all possible probing sequences. Our approach is based on Kalman Filtering theory which allows to integrate estimates obtained from the measurements and the extrapolative calculations. The variances in the estimates can provably stay below the specified level throughout the journey. 
 
To the best of our knowledge, these results appear to be the first that uses a mathematically rigorous approach to minimize the probing cost while guaranteeing the quality of estimates in personal positioning.
45563418
Video extraction in compressed domain
In this paper, we propose a video extraction algorithm directly in the compressed domain for low cost and fast content access to those compressed video data via MPEG. Extensive experiments show that such extracted images and videos not only maintain well-preserved content features, but also illustrate reasonable quality in terms of both PSNR values and visual inspection. In cases where video processing tasks do not necessarily require full resolution pixel data such as browsing, pattern recognition, and object tracking in surveillance applications, the proposed algorithm will provide superior performance in terms of computing efficiency, under the context that millions of video frames need to be accessed, yet they are stored in compressed format.
15412274
A study of Poisson query generation model for information retrieval
Many variants of language models have been proposed for information retrieval. Most existing models are based on multinomial distribution and would score documents based on query likelihood computed based on a query generation probabilistic model. In this paper, we propose and study a new family of query generation models based on Poisson distribution. We show that while in their simplest forms, the new family of models and the existing multinomial models are equivalent. However, based on different smoothing methods, the two families of models behave differently. We show that the Poisson model has several advantages, including naturally accommodating per-term smoothing and modeling accurate background more efficiently. We present several variants of the new model corresponding to different smoothing methods, and evaluate them on four representative TREC test collections. The results show that while their basic models perform comparably, the Poisson model can out perform multinomial model with per-term smoothing. The performance can be further improved with two-stage smoothing.
29388694
A fuzzy logic approach for detection of video shot boundaries
Video temporal segmentation is normally the first and important step for content-based video applications. Many features including the pixel difference, colour histogram, motion, and edge information etc. have been widely used and reported in the literature to detect shot cuts inside videos. Although existing research on shot cut detection is active and extensive, it still remains a challenge to achieve accurate detection of all types of shot boundaries with one single algorithm. In this paper, we propose a fuzzy logic approach to integrate hybrid features for detecting shot boundaries inside general videos. The fuzzy logic approach contains two processing modes, where one is dedicated to detection of abrupt shot cuts including those short dissolved shots, and the other for detection of gradual shot cuts. These two modes are unified by a mode-selector to decide which mode the scheme should work on in order to achieve the best possible detection performances. By using the publicly available test data set from Carleton University, extensive experiments were carried out and the test results illustrate that the proposed algorithm outperforms the representative existing algorithms in terms of the precision and recall rates.
967597
An exploration of axiomatic approaches to information retrieval
Existing retrieval models generally do not offer any guarantee for optimal retrieval performance. Indeed, it is even difficult, if not impossible, to predict a model's empirical performance analytically. This limitation is at least partly caused by the way existing retrieval models are developed where relevance is only coarsely modeled at the level of documents and queries as opposed to a finer granularity level of terms. In this paper, we present a new axiomatic approach to developing retrieval models based on direct modeling of relevance with formalized retrieval constraints defined at the level of terms. The basic idea of this axiomatic approach is to search in a space of candidate retrieval functions for one that can satisfy a set of reasonable retrieval constraints. To constrain the search space, we propose to define a retrieval function inductively and decompose a retrieval function into three component functions. Inspired by the analysis of the existing retrieval functions with the inductive definition, we derive several new retrieval functions using the axiomatic retrieval framework. Experiment results show that the derived new retrieval functions are more robust and less sensitive to parameter settings than the existing retrieval functions with comparable optimal performance.
17735204
3D Face Reconstruction Under Imperfect Tracking Circumstances Using Shape Model Constraints
We propose a factorization structure from motion (SfM) framework which employs 3D active shape constraints for a 3D face model application. Two types of shape model, individual shape models and a generic model, are used to approximate non-linear manifold variation. When the 3D shape models are trained, they help the SfM algorithm to reconstruct the 3D face structure under noisy observation (tracking) circumstances. By minimizing two sets of errors, the reconstruction error generated by the linear transform of the shape models and projection error obtained by re-projecting the 3D shape to 2D positions, the 3D face shapes can be recovered optimally. Experimental results show that this algorithm accurately reconstructs the 3D shape of familiar and non-familiar faces from video sequences under circumstances of imperfect face tracking or noisy observations.