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| Author(s): |
Glauco Vitor Pedrosa
Total Authors: 1
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| Document type: | Doctoral Thesis |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) |
| Defense date: | 2015-08-24 |
| Examining board members: |
Agma Juci Machado Traina;
Célia Aparecida Zorzo Barcelos;
Marcos Aurélio Batista;
Alexandre Xavier Falcão;
Aparecido Nilceu Marana
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| Advisor: | Agma Juci Machado Traina |
| Abstract | |
The automatic similarity analysis between images depends heavily on the use of descriptors that should be able to characterize the images\' content in compact and discriminative features. These extracted features are represented by a feature-vector employed to represent the images in the process of mining and analysis for classification and/or retrieval. This work investigated the use of visual dictionaries and context to represent and retrieve the local image features using extended formalism with high descriptive power. This thesis presents three new proposals that contribute in advancing the state-of-the-art by developing new methodologies for characterizing images and for processing similarity queries by content. The first proposal extends the Bag-of-Visual-Words model, by encoding the interaction between the visual words and their spatial arrangements in the image space. For this, three new techniques are presented: (i) Weighted Histogram (WE); (ii) Bunch-of--grams and (iii) Global Spatial Arrangement (GSA). These three techniques allow to extract additional semantically information that enrich the final image representation described in visual-words. The second proposal introduces a new descriptor, called Bag-of-Salience-Points (BoSP), which characterizes and analyzes the dissimilarity of shapes (silhouettes) exploring their salient point. The BoSP descriptor is based on using a dictionary of curvatures and spatial-histograms to represent succinctly the saliences of a shape into a single fixed-length feature-vector, allowing to retrieve shapes using distance functions computationally fast. Finally, the third proposal introduces a new similarity query model, called Similarity based on Dominant Images (SimDIm), based on the concept of dominant images, which is a set of images representing the entire collection of images of the database in a more diversified and reduced manner. This concept allows to efficiently analyze the context of the entire collection, which is the final goal. The experiments showed that the proposed methods effectively contributed to characterize and quantify the similarity between images using extended approaches based on visual dictionaries and contextual analysis, reducing the semantic gap between human perception and computational description. (AU) | |
| FAPESP's process: | 11/21460-4 - Study and Definition of the Bag-of-Features Approach for Retrieval by Content of Medical Images Complying with the Specialists Expectations |
| Grantee: | Glauco Vitor Pedrosa |
| Support Opportunities: | Scholarships in Brazil - Doctorate |