Human expert modelling using semantics-oriented video retrieval for surveillance in hard industry

Abstract

The surveillance application aims at improving the quality of technology via modelling human expert behaviour in the coking plant Mittal Steel Ostrava, the Czech Republic. Video data on several industrial processes are captured by means of a CCD camera and classified by using Latent Semantic Indexing (LSI) with the respect to etalons classified by an expert. We also investigate the possibility to combine LSI with color histogram analysis, using experience from our prior research on web image classification. partial symmetric eigenproblem, very large systems of linear equations, iterative solvers

Linear algebra for vision-based surveillance in heavy industry - convergence behavior case study

“…In our approach [8,9,10,11], a raster image is coded as a sequence of pixels. Then the coded image can be understood as a vector of a m-dimensional space, where m denotes the number of pixels (attributes).…”

“…For this reason, the number of extreme singular values and associated singular vectors used for LSI was estimated experimentally according to the distribution of singular values of the document matrix, see Figure 1. We have extended experiments [10] by a subjective evaluation of results related to these two different settings: In the first case, k = 8 largest singular values is assumed, whereas in the second case k = 45 largest singular values were used for LSI. For each experiment, query image represents a different industrial process.…”