Environmental Relations in Image Understanding: The Force of Gravity

This paper shows how assumptions and information concerning the external world properties of horizontal and vertical can aid in the analysis of images, even at the very lowest levels of processing. First, the author reviews the pervasiveness of the force of gravity, and its influence on most natural image understanding systems. Next, he derives several fundamental mathematical results relating phenomena in both the gradient space and the images space to the external world attributes of horizontal and vertical. He then shows how these results interrelate three imaging phenomena: the surfaces in the image, the external sensor parameters, and the environmental labels. It is detailed how, in general, specific information regarding any two of these phenomena can be used to quantitatively derive the third; occasionally one can do even better. Algorithms for such quantitative derivations are presented, including two based on the Hough transform. The author further shows how certain environmental perpendicularities can be exploited very efficiently, and even elegantly: ordinarily complex math simplifies to the extent that environmental distances can be directly read off the image. The power of such environmental labels is then demonstrated by an analysis of the source of ambiguity in a simple illusion-like image configuration. The paper concludes with an analysis of the class of heuristics that have been invoked throughout. They are seen to be instantiations of the shape-from-texture meta-heuristics that "near implies preferred" and "preferred implies simple.

Analysis and Visualization of Index Words from Audio Transcripts of Instructional Videos

We introduce new techniques for extracting, analyzing, and visualizing textual contents from instructional videos of low production quality. Using Automatic Speech Recognition, approximate transcripts (H75% Word Error Rate) are obtained from the originally highly compressed videos of university courses, each comprising between 10 to 30 lectures. Text material in the form of books or papers that accompany the course are then used to filter meaningful phrases from the seemingly incoherent transcripts. The resulting index into the transcripts is tied together and visualized in 3 experimental graphs that help in understanding the overall course structure and provide a tool for localizing certain topics for indexing. We specifically discuss a Transcript Index Map, which graphically lays out key phrases for a course, a Textbook Chapter to Transcript Match, and finally a Lecture Transcript Similarity graph, which clusters semantically similar lectures. We test our methods and tools on 7 full courses with 230 hours of video and 273 transcripts. We are able to extract up to 98 unique key terms for a given transcript and up to 347 unique key terms for an entire course. The accuracy of the Textbook Chapter to Transcript Match exceeds 70% on average. The methods used can be applied to genres of video in which there are recurrent thematic words (news, sports, meetings,...)Comment: 2004 IEEE International Workshop on Multimedia Content-based Analysis and Retrieval; 20 pages, 8 figures, 7 table

Optimizing Frequency Queries for Data Mining Applications

Data mining algorithms use various Trie and bitmap-based representations to optimize the support (i.e., frequency) counting performance. In this paper, we compare the memory requirements and support counting performance of FP Tree, and Compressed Patricia Trie against several novel variants of vertical bit vectors. First, borrowing ideas from the VLDB domain, we compress vertical bit vectors using WAH encoding. Second, we evaluate the Gray code rank-based transaction reordering scheme, and show that in practice, simple lexicographic ordering, obtained by applying LSB Radix sort, outperforms this scheme. Led by these results, we propose HDO, a novel Hamming-distance-based greedy transaction reordering scheme, and aHDO, a linear-time approximation to HDO. We present results of experiments performed on 15 common datasets with varying degrees of sparseness, and show that HDO- reordered, WAH encoded bit vectors can take as little as 5% of the uncompressed space, while aHDO achieves similar compression on sparse datasets. Finally, with results from over a billion database and data mining style frequency query executions, we show that bitmap-based approaches result in up to hundreds of times faster support counting, and HDO-WAH encoded bitmaps offer the best space-time tradeoff

High Quality, Efficient Hierarchical Document Clustering Using Closed Interesting Itemsets

High dimensionality remains a significant challenge for document clustering. Recent approaches used frequent itemsets and closed frequent itemsets to reduce dimensionality, and to improve the efficiency of hierarchical document clustering. In this paper, we introduce the notion of "closed interesting" itemsets (i.e. closed itemsets with high interestingness). We provide heuristics such as "super item" to efficiently mine these itemsets and show that they provide significant dimensionality reduction over closed frequent itemsets. Using "closed interesting" itemsets, we propose a new hierarchical document clustering method that outperforms state of the art agglomerative, partitioning and frequent-itemset based methods both in terms of FScore and Entropy, without requiring dataset specific parameter tuning. We evaluate twenty interestingness measures on nine standard datasets and show that when used to generate "closed interesting" itemsets, and to select parent nodes, Mutual Information, Added Value, Yule's Q and Chi-Square offers best clustering performance, regardless of the characteristics of underlying dataset. We also show that our method is more scalable, and results in better run-time performance as compare to leading approaches. On a dual processor machine, our method scaled sub-linearly and was able to cluster 200K documents in about 40 seconds

SIMD Tree Algorithms for Image Correlation

This paper examines the applicability of fine-grained tree-structured SIMD machines, which are amenable to highly efficient VLSI implementation to image correlation which is a representative of image window-based operations. Several algorithms are presented for image shifting and correlation operations. A particular massively parallel machine called NON-VON is used for purposes of explication and performance evaluation. Although the most recent version of the NON-VON architecture also supports other interconnection topologies and execution modes, only its tree-structured communication capabilities and its SIMD mode of execution are considered in this paper. Novel algorithmic techniques are described, such as vertical pipelining, subproblem partitioning, associative matching, and data duplication that effectively exploit the massive parallelism available in fine-grained SIMD tree machines while avoiding communication bottlenecks. Simulation results are presented and compared with results obtained or forecast for other highly parallel machines. The relative advantages and limitations of the class of machines under consideration are then outlined