7586483
Video search reranking via information bottleneck principle
We propose a novel and generic video/image reranking algorithm, IB reranking, which reorders results from text-only searches by discovering the salient visual patterns of relevant and irrelevant shots from the approximate relevance provided by text results. The IB reranking method, based on a rigorous Information Bottleneck (IB) principle, finds the optimal clustering of images that preserves the maximal mutual information between the search relevance and the high-dimensional low-level visual features of the images in the text search results. Evaluating the approach on the TRECVID 2003-2005 data sets shows significant improvement upon the text search baseline, with relative increases in average performance of up to 23%. The method requires no image search examples from the user, but is competitive with other state-of-the-art example-based approaches. The method is also highly generic and performs comparably with sophisticated models which are highly tuned for specific classes of queries, such as named-persons. Our experimental analysis has also confirmed the proposed reranking method works well when there exist sufficient recurrent visual patterns in the search results, as often the case in multi-source news videos.
269822
Visual Cue Cluster Construction via Information Bottleneck Principle and Kernel Density Estimation
Recent research in video analysis has shown a promising direction, in which mid-level features (e.g., people, anchor, indoor) are abstracted from low-level features (e.g., color, texture, motion, etc.) and used for discriminative classification of semantic labels. However, in most systems, such mid-level features are selected manually. In this paper, we propose an information-theoretic framework, visual cue cluster construction (VC3), to automatically discover adequate mid-level features. The problem is posed as mutual information maximization, through which optimal cue clusters are discovered to preserve the highest information about the semantic labels. We extend the Information Bottleneck framework to high-dimensional continuous features and further propose a projection method to map each video into probabilistic memberships over all the cue clusters. The biggest advantage of the proposed approach is to remove the dependence on the manual process in choosing the mid-level features and the huge labor cost involved in annotating the training corpus for training the detector of each mid-level feature. The proposed VC3 framework is general and effective, leading to exciting potential in solving other problems of semantic video analysis. When tested in news video story segmentation, the proposed approach achieves promising performance gain over representations derived from conventional clustering techniques and even the mid-level features selected manually.
16227528
Fast image retrieval using color-spatial information
Abstract. In this paper, we present an image retrieval system that employs both the color and spatial information of images to facilitate the retrieval process. The basic unit used in our technique is a single-colored cluster, which bounds a homogeneous region of that color in an image. Two clusters from two images are similar if they are of the same color and overlap in the image space. The number of clusters that can be extracted from an image can be very large, and it affects the accuracy of retrieval. We study the effect of the number of clusters on retrieval effectiveness to determine an appropriate value for “optimal'' performance. To facilitate efficient retrieval, we also propose a multi-tier indexing mechanism called the Sequenced Multi-Attribute Tree (SMAT). We implemented a two-tier SMAT, where the first layer is used to prune away clusters that are of different colors, while the second layer discriminates clusters of different spatial locality. We conducted an experimental study on an image database consisting of 12,000 images. Our results show the effectiveness of the proposed color-spatial approach, and the efficiency of the proposed indexing mechanism.
4516498
Using Instant Messaging to Provide an Intelligent Learning Environment
Instant Messaging enables learners and educators to interact in an on-line environment. In this paper, we propose an intelligent ChatBot system, based on instant messaging, for student on-line coaching in an English learning environment. The proposed ChatBot facilitates synchronous communication with students by using ready reference materials including, dictionaries, authorized conversation material with speaking, and a question-answering function. The agent records and analyzes conversations so that the teacher can assess students' progress. Our contribution in this paper is that we integrate the NLP Tool and AIML into an instant messaging-based ChatBot for English as a Second Language programs.
6185076
Text categorization using automatically acquired domain ontology
In this paper, we describe ontology-based text categorization in which the domain ontologies are automatically acquired through morphological rules and statistical methods. The ontology-based approach is a promising way for general information retrieval applications such as knowledge management or knowledge discovery. As a way to evaluate the quality of domain ontologies, we test our method through several experiments. Automatically acquired domain ontologies, with or without manual editing, have been used for text categorization. The results are quite satisfactory. Furthermore, we have developed an automatic method to evaluate the quality of our domain ontology.
38278026
A Simple Test for the Consecutive Ones Property
A (0,1)-matrix satisfies the consecutive ones property if there exists a column permutation such that the ones in each row of the resulting matrix are consecutive. Booth and Lueker (1976, J. Comput. System Sci.13, 335?378) designed a linear time-testing algorithm for this property based on a data structure called “PQ-trees.” This procedure is quite complicated and the linear-time-amortized analysis is also rather involved. We developed an off-line linear time test for the consecutive ones property without using PQ-trees and the corresponding template matching, which makes ours considerably simpler. A simplification of the consecutive ones test will immediately simplify algorithms (and computer codes) for interval-graph and planar-graph recognition. Our approach is based on a decomposition technique that separates the rows into prime subsets, each of which admits essentially a unique column ordering that realizes the consecutive ones property. The success of this approach is based on finding a good “row ordering” to be tested iteratively.
1251407
Reference metadata extraction using a hierarchical knowledge representation framework
The integration of bibliographical information on scholarly publications available on the Internet is an important task in the academic community. Accurate reference metadata extraction from such publications is essential for the integration of metadata from heterogeneous reference sources. In this paper, we propose a hierarchical template-based reference metadata extraction method for scholarly publications. We adopt a hierarchical knowledge representation framework called INFOMAP, which automatically extracts metadata. The experimental results show that, by using INFOMAP, we can extract author, title, journal, volume, number (issue), year, and page information from different kinds of reference styles with a high degree of precision. The overall average accuracy is 92.39% for the six major reference styles compared in this study.