Object recognition based on bag of features and a new local pattern descriptor
| Autor: | Marcos Rosa, Carolina Toledo Ferraz, Osmando Pereira Junior, Adilson Gonzaga |
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| Rok vydání: | 2014 |
| Předmět: |
Pixel
business.industry Local binary patterns ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Cognitive neuroscience of visual object recognition Codebook Scale-invariant feature transform Pattern recognition Object (computer science) Artificial Intelligence Test set Image scaling Computer vision Computer Vision and Pattern Recognition Artificial intelligence VISÃO COMPUTACIONAL business Software Mathematics |
| Zdroj: | Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP |
| Popis: | Bag of Features (BoF) has gained a lot of interest in computer vision. Visual codebook based on robust appearance descriptors extracted from local image patches is an effective means of texture analysis and scene classification. This paper presents a new method for local feature description based on gray-level difference mapping called Mean Local Mapped Pattern (M-LMP). The proposed descriptor is robust to image scaling, rotation, illumination and partial viewpoint changes. The training set is composed of rotated and scaled images, with changes in illumination and view points. The test set is composed of rotated and scaled images. The proposed descriptor more effectively captures smaller differences of the image pixels than similar ones. In our experiments, we implemented an object recognition system based on the M-LMP and compared our results to the Center-Symmetric Local Binary Pattern (CS-LBP) and the Scale-Invariant Feature Transform (SIFT). The results for object classification were analyzed in a BoF methodology and show that our descriptor performs better compared to these two previously published methods. |
| Databáze: | OpenAIRE |
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