9568730
Exploring video content structure for hierarchical summarization
Abstract.In this paper, we propose a hierarchical video summarization strategy that explores video content structure to provide the users with a scalable, multilevel video summary. First, video-shot- segmentation and keyframe-extraction algorithms are applied to parse video sequences into physical shots and discrete keyframes. Next, an affinity (self-correlation) matrix is constructed to merge visually similar shots into clusters (supergroups). Since video shots with high similarities do not necessarily imply that they belong to the same story unit, temporal information is adopted by merging temporally adjacent shots (within a specified distance) from the supergroup into each video group. A video-scene-detection algorithm is thus proposed to merge temporally or spatially correlated video groups into scenario units. This is followed by a scene-clustering algorithm that eliminates visual redundancy among the units. A hierarchical video content structure with increasing granularity is constructed from the clustered scenes, video scenes, and video groups to keyframes. Finally, we introduce a hierarchical video summarization scheme by executing various approaches at different levels of the video content hierarchy to statically or dynamically construct the video summary. Extensive experiments based on real-world videos have been performed to validate the effectiveness of the proposed approach.
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.
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.
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.
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.
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.
41218825
Relaxed game chromatic number of graphs
This paper discusses a coloring game on graphs. Let k, d be non-negative integers and C a set of k colors. Two persons, Alice and Bob, alternately color the vertices of G with colors from C, with Alice having the first move. A color i is legal for an uncolored vertex x if by coloring x with color i, the subgraph of G induced by those vertices of color i has maximum degree at most d. Each move of Alice or Bob colors an uncolored vertex with a legal color. The game is over if either all vertices are colored, or no more vertices can be colored with a legal color. Alice's goal is to produce a legal coloring which colors all the vertices of G, and Bob's goal is to prevent this from happening. We shall prove that if G is a forest, then for k = 3, d ≥ 1, Alice has a winning strategy. If G is an outerplanar graph, then for k = 6 and d ≥ 1, Alice has a winning strategy.
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.
12426657
Ramsey families which exclude a graph
For graphsA andB the relationA→(B)r1 means that for everyr-coloring of the vertices ofA there is a monochromatic copy ofB inA. Forb (G) is the family of graphs which do not embedG. A familyℱof graphs is Ramsey if for all graphsB∈ℱthere is a graphA∈ℱsuch thatA→(B)r1. The only graphsG for which it is not known whether Forb (G) is Ramsey are graphs which have a cutpoint adjacent to every other vertex except one. In this paper we prove for a large subclass of those graphsG, that Forb (G) does not have the Ramsey property.
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.
10041607
Comparison of Modularization Methods in Application to Different Biological Networks
Most biological networks have been proposed to possess modular organization, which increases the robustness, flexibility, and stability of networks. Many clustering methods have been used in mining biological data and partitioning complex networks into functional modules. Most of these methods require presetting the number of modules and therefore can potentially obtain biased results. The Markov clustering method (MCL) and the simulated annealing module-detection method (SA) eliminate this requirement and can objectively separate relatively dense subgraphs. In this paper, we compared these two module-detection methods for three types of biological data: protein family classification, microarray clustering, and modularity of metabolic networks. We found that these two methods show differential advantages for different biological networks. In the case of the gene network based on Affymetrix microarray spike data, MCL exactly identified the same number of groups and same contents in each group set by the spike data. In the case of the gene network derived from actual expression data, although neither of the two methods can perfectly recover the natural classification, MCL performs slightly better than SA. However, with increased random noise added to the gene expression values, SA generates better modular structures with higher modularity. Next we compared the modularization results of MCL and SA for protein family classification and found the modules detected by SA could not be well matched with the Structural Classification of Proteins (SCOP database), which suggests that MCL is ideally suited to the rapid and accurate detection of protein families. In addition, we used both methods to detect modules in the metabolic network of E. coli. MCL gives a trivial clustering, which generates biologically insignificant modules. In contrast, SA detects modules well corresponding to the KEGG functional classification. Moreover the modularity for several other metabolic networks detected by SA is also much higher than that by MCL. In summary, MCL is more suited to modularize relatively complete and definite data, such as a protein family network. In contrast, SA is less sensitive to noise such as experimental error or incomplete data and outperforms MCL when modularizing gene networks based on microarray data and large scale metabolic networks constructed from incomplete databases.