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Title: Biologically-inspired building recognition
Author: Li, Jing
ISNI:       0000 0004 2742 6079
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2012
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Building recognition has attracted much attention in computer vision research. However, existing building recognition systems have the following problems: I) extracted features are not biologically-related to human visual perception; 2) features are usually of high dimensionality, resulting in the curse of dimensionality; 3) semantic gap between low- level visual features and high-level image concepts; and 4) limited challenges set by published databases. To address the aforementioned problems, this thesis proposes two biologically-inspired building recognition schemes and creates a new building image database, i.e., the Sheffield Building Image Dataset. We propose the biologically-plausible building recognition (BPBR) scheme based on biologically-inspired features that can model the process of human visual perception. To deal with the curse of dimensionality, the dirnensionality of extracted features is reduced by linear discriminant analysis (LOA). Afterwards, classification is conducted by the nearest neighbour rule and the recognition rate is 85.25%, which is 11.93% higher than that of the hierarchical building recognition system. To fill the semantic gap, BPBR is further enhanced by applying a relevance feedback technique after dirnensionality reduction (OR) and a support vector machine (SYM) for classification. The recognition rate of the enhanced BPBR is 93.13%, which is 7.88% superior to the original BPBR scheme. In addition, different OR techniques are examined to find out how they affect building recognition performance. Motivated by the popularity of local features, we develop the local feature-based building recognition (LFBR) scheme. LFBR applies steerable filters to feature representation and utilizes max pooling to achieve compact representation and robustness to noise. After that, LOA is utilized for dimensionality reduction and recognition is implemented by a SYM. Compared with BPBR, LFBR is much better and achieves the performance of 94.66%. Based on a large number of statistical experiments on Sheffield Building Image Dataset, the indications are that our proposed schemes outperform the state-of-the-art building recognition systems in terms of accuracy and efficiency.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available