31920818
Automatic model-based semantic object extraction algorithm
Automatic image segmentation and object extraction play an important role in supporting content-based image coding, indexing, and retrieval. However, the low-level visual homogeneity critical (like color, texture, intensity, and so on) for segmentation do not lead to semantic objects directly because a semantic object can contain totally different gray levels, color, or texture. We propose an automatic model-based semantic object extraction algorithm by integrating object seeds with their region constraint graphs (perceptual models). Images are first partitioned into a set of homogeneous regions with accurate boundaries by integrating the results obtained by similarity-based region growing and edge detection procedures. We propose a 1-D fast entropic thresholding technique for determining the thresholds used in region growing and edge detection automatically. The object seeds, which are the intuitive and representative parts of semantic objects, are then distinguished from these homogeneous image regions. A seeded region aggregation procedure is used for merging the adjacent regions of a detected object seed to give a semantic object according to the perceptual model of the object. We focus on semantic human object generation by taking faces as object seeds and using a ratio-based perceptual model.
5621999
Accelerated Kernel Feature Analysis
A fast algorithm, Accelerated Kernel Feature Analysis (AKFA), that discovers salient features evidenced in a sample of n unclassified patterns, is presented. Like earlier kernel-based feature selection algorithms, AKFA implicitly embeds each pattern into a Hilbert space, H, induced by a Mercer kernel. An \ell-dimensional linear subspace of H is iteratively constructed by maximizing a variance condition for the nonlinearly transformed sample. This linear subspace can then be used to define more efficient data representations and pattern classifiers. AKFA requires O(\elln2) operations, as compared to 0(n^3) for Sch¨olkof, Smola, and M¨uller’s Kernel Principal Component Analysis (KPCA), and O(\ell^2 n^2) for Smola, Mangasarian, and Sch¨olkopf’s Sparse Kernel Feature Analysis (SKFA). Numerical experiments show that AKFA can generate more concise feature representations than both KPCA and SKFA, and demonstrate that AKFA obtains similar classification performance as KPCA for a face recognition problem.
14899560
Dealing with Predictive-but-Unpredictable Attributes in Noisy Data Sources
Attribute noise can affect classification learning. Previous work in handling attribute noise has focused on those predictable attributes that can be predicted by the class and other attributes. However, attributes can often be predictive but unpredictable. Being predictive, they are essential to classification learning and it is important to handle their noise. Being unpredictable, they require strategies different from those of predictable attributes. This paper presents a study on identifying, cleansing and measuring noise for predictive-but-unpredictable attributes. New strategies are accordingly proposed. Both theoretical analysis and empirical evidence suggest that these strategies are more effective and more efficient than previous alternatives.
11232317
Cost-constrained data acquisition for intelligent data preparation
Real-world data is noisy and can often suffer from corruptions or incomplete values that may impact the models created from the data. To build accurate predictive models, data acquisition is usually adopted to prepare the data and complete missing values. However, due to the significant cost of doing so and the inherent correlations in the data set, acquiring correct information for all instances is prohibitive and unnecessary. An interesting and important problem that arises here is to select what kinds of instances to complete so the model built from the processed data can receive the "maximum" performance improvement. This problem is complicated by the reality that the costs associated with the attributes are different, and fixing the missing values of some attributes is inherently more expensive than others. Therefore, the problem becomes that given a fixed budget, what kinds of instances should be selected for preparation, so that the learner built from the processed data set can maximize its performance? In this paper, we propose a solution for this problem, and the essential idea is to combine attribute costs and the relevance of each attribute to the target concept, so that the data acquisition can pay more attention to those attributes that are cheap in price but informative for classification. To this end, we will first introduce a unique economical factor (EF) that seamlessly integrates the cost and the importance (in terms of classification) of each attribute. Then, we will propose a cost-constrained data acquisition model, where active learning, missing value prediction, and impact-sensitive instance ranking are combined for effective data acquisition. Experimental results and comparative studies from real-world data sets demonstrate the effectiveness of our method.
15136742
Mining Complex Patterns across Sequences with Gap Requirements
The recurring appearance of sequential patterns, when confined by the predefined gap requirements, often implies strong temporal correlations or trends among pattern elements. In this paper, we study the problem of mining a set of gap constrained sequential patterns across multiple sequences. Given a set of sequences S1, S2,., SK constituting a single hypersequence S, we aim to find recurring patterns in S, say P, which may cross multiple sequences with all their matching characters in S bounded by the user specified gap constraints. Because of the combinatorial candidate explosion, traditional Apriori-based algorithms are computationally infeasible. Our research proposes a new mechanism to ensure pattern growing and pruning. When combining the pruning technique with our Gap Constrained Search (GCS) and map-based support prediction approaches, our method achieves a speed about 40 times faster than its other peers.
3813357
Video data mining: semantic indexing and event detection from the association perspective
Advances in the media and entertainment industries, including streaming audio and digital TV, present new challenges for managing and accessing large audio-visual collections. Current content management systems support retrieval using low-level features, such as motion, color, and texture. However, low-level features often have little meaning for naive users, who much prefer to identify content using high-level semantics or concepts. This creates a gap between systems and their users that must be bridged for these systems to be used effectively. To this end, in this paper, we first present a knowledge-based video indexing and content management framework for domain specific videos (using basketball video as an example). We will provide a solution to explore video knowledge by mining associations from video data. The explicit definitions and evaluation measures (e.g., temporal support and confidence) for video associations are proposed by integrating the distinct feature of video data. Our approach uses video processing techniques to find visual and audio cues (e.g., court field, camera motion activities, and applause), introduces multilevel sequential association mining to explore associations among the audio and visual cues, classifies the associations by assigning each of them with a class label, and uses their appearances in the video to construct video indices. Our experimental results demonstrate the performance of the proposed approach.
7395446
Segmenting hands of arbitrary color
Hand segmentation is a prerequisite for many gesture recognition tasks. Color has been widely used for hand segmentation. However, many approaches rely on predefined skin color models. It is very difficult to predefine a color model in a mobile application where the light condition may change dramatically over time. We propose a novel statistical approach to hand segmentation based on Bayes decision theory. The proposed method requires no predefined skin color model. Instead it generates a hand color model and a background color model for a given image, and uses these models to classify each pixel in the image as either a hand pixel or a background pixel. Models are generated using a Gaussian mixture model with the restricted EM algorithm. Our method is capable of segmenting hands of arbitrary color in a complex scene. It performs well even when there is a significant overlap between hand and background colors, or when the user wears gloves. We show that the Bayes decision method is superior to a commonly used method by comparing their upper bound performance. Experimental results demonstrate the feasibility of the proposed method.
1251013
Congestion-aware rate allocation for multipath video streaming over ad hoc wireless networks
For multipath video streaming over ad hoc wireless networks, received video quality is influenced by both the encoder performance and the delayed packet arrivals due to limited bandwidth. We propose a rate allocation scheme to optimize the expected received video quality based on simple models of encoder rate-distortion performance and network rate-congestion tradeoffs. Given the embedded bit-streams of a group of video frames and the cross traffic condition on a given set of paths, a gradient descent method is used to determine the amount of bits to be transmitted over the multiple paths for each frame. Two rate allocation strategies, layered representation and partial repetition are also investigated. Simulation results of video streaming over a 15-node wireless ad hoc network demonstrates the benefit of performing rate allocation optimization over a simple heuristic scheme. It is also shown that while layered representation is more efficient for mild delay constraints, partial repetition is more robust against packet losses.
52810083
A hierarchical modeling framework for on-chip communication architectures
The communication sub-system of complex IC systems is increasingly critical for achieving system performance. Given this, it is important that the on-chip communication architecture should be included in any quantitative evaluation of system design during design space exploration. While there are several mature methodologies for the modeling and evaluation of architectures of processing elements, there is relatively little work done in modeling of an extensive range of on-chip communication architectures, and integrating this into a single modeling and simulation environment combining processing element and on-chip communication architectures. This paper describes a modeling framework with accompanying simulation tools that attempts to fill this gap. Based on an analysis of a wide range of on-chip communication architectures, we describe how a specific hierarchical class library can be used to develop new on-chip communication architectures, or variants of existing ones with relatively little incremental effort. We demonstrate this through three case studies including two commercial on-chip bus systems and an on-chip packet switching network. Here we show that through careful analysis and construction it is possible for the modeling environment to support the common features of these architectures as part of the library and permit instantiation of the individual architectures as variants of the library design. As part of this methodology we also show how different levels of abstraction of the model can be supported and viewed as different variants that can be used in an accuracy versus simulation time trade-off.
12133267
A note on graph reconstruction
Suppose G and G′ are graphs on the same vertex set V such that for each x ∈ V there is an isomorphism θx of G−x to G′−x. We prove in this paper that if there is a vertex x ∈ V and an automorphism σ of G − x such that θx agrees with σ on all except for at most three vertices of V −x, then G is isomorphic to G′. As a corollary we prove that if a graph G has a vertex which is contained in at most three bad pairs, then G is reconstructible. Here a pair of vertices x, y of a graph G is called a bad pair if there exist u, v ∈ V (G) such that {u, v} 6 = {x, y} and G− {x, y} is isomorphic to G− {u, v}. All graphs discussed here are finite simple graphs. The vertex set and edge set of a graph G are denoted by V (G) and E(G) respectively. If A is a subset of V (G), we use G|A and G−A to denote the subgraphs of G induced by A and V (G)−A respectively. When A = {x} is a singleton, we use G−x instead of G−{x}. For two subsets X, Y of V (G), we use eG(X, Y ) to denote the number of edges joining a vertex of X to a vertex of Y . For brevity, we write eG(x, X) for eG({x}, X) and eG(X) for eG(X, X). The degree of x in G is denoted by dG(x). We shall use some notations defined in [4]. Two graphs G and H are hypomorphic if there exists a bijection f : V (G) → V (H) such that G− x is ∗Current address: SFB 343, Universität Bielefeld, D-33501 Bielefeld, FRG
1786671
Light field compression using disparity-compensated lifting and shape adaptation
We propose disparity-compensated lifting for wavelet compression of light fields. With this approach, we obtain the benefits of wavelet coding, such as scalability in all dimensions, as well as superior compression performance. Additionally, the proposed approach solves the irreversibility limitations of previous light field wavelet coding approaches, using the lifting structure. Our scheme incorporates disparity compensation into the lifting structure for the transform across the views in the light field data set. Another transform is performed to exploit the coherence among neighboring pixels, followed by a modified SPIHT coder and rate-distortion optimized bitstream assembly. A view-sequencing algorithm is developed to organize the views for encoding. For light fields of an object, we propose to use shape adaptation to improve the compression efficiency and visual quality of the images. The necessary shape information is efficiently coded based on prediction from the existing geometry model. Experimental results show that the proposed scheme exhibits superior compression performance over existing light field compression techniques.