Improving a Discriminative Approach to Object Recognition Using Image Patches

2005 ◽  
pp. 326-333 ◽  
Author(s):  
Thomas Deselaers ◽  
Daniel Keysers ◽  
Hermann Ney
Keyword(s):  
Object Recognition ◽  
Image Patches ◽  
Discriminative Approach

scholarly journals Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector

2018 ◽  
pp. 502-510 ◽  
Author(s):  
Sumedha Singla ◽  
Mingming Gong ◽  
Siamak Ravanbakhsh ◽  
Frank Sciurba ◽  
Barnabas Poczos ◽  
...  
Keyword(s):  
Image Patches ◽  
Discriminative Approach

OBJECT RECOGNITION BASED ON BAG OF FEATURES AND A NEW LOCAL PATTERN DESCRIPTOR

2014 ◽  
Vol 28 (08) ◽  
pp. 1455010 ◽  
Author(s):  
CAROLINA TOLEDO FERRAZ ◽  
OSMANDO PEREIRA ◽  
MARCOS VERDINI ROSA ◽  
ADILSON GONZAGA
Keyword(s):  
Object Recognition ◽  
Effective Means ◽  
Recognition System ◽  
Scale Invariant ◽  
Image Scaling ◽  
Bag Of Features ◽  
Image Patches ◽  
Image Pixels ◽  
Scale Invariant Feature ◽  
Local Image

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.

215 Object recognition using the compositions of image patches

2011 ◽  
Vol 2011.49 (0) ◽  
pp. 61-62
Author(s):  
Jyun_ichi Katsura ◽  
Katsunobu Konishi
Keyword(s):  
Object Recognition ◽  
Image Patches

Robust object recognition using a color co-occurrence histogram and the spatial relations of image patches

2009 ◽  
Vol 13 (2) ◽  
pp. 488-492 ◽  
Author(s):  
Heebeom Bang ◽  
Sanghoon Lee ◽  
Dongjin Yu ◽  
Il Hong Suh
Keyword(s):  
Object Recognition ◽  
Spatial Relations ◽  
Image Patches

Use It and Still Lose It?

GeroPsych ◽  
2010 ◽  
Vol 23 (3) ◽  
pp. 169-175 ◽  
Author(s):  
Adrian Schwaninger ◽  
Diana Hardmeier ◽  
Judith Riegelnig ◽  
Mike Martin
Keyword(s):  
Object Recognition ◽  
Cognitive Development ◽  
Recognition Test ◽  
Cognitive Aging ◽  
Recognition Task ◽  
Airport Security ◽  
Visual Object ◽  
Long Term Effects ◽  
Object Recognition Test ◽  
X Ray

In recent years, research on cognitive aging increasingly has focused on the cognitive development across middle adulthood. However, little is still known about the long-term effects of intensive job-specific training of fluid intellectual abilities. In this study we examined the effects of age- and job-specific practice of cognitive abilities on detection performance in airport security x-ray screening. In Experiment 1 (N = 308; 24–65 years), we examined performance in the X-ray Object Recognition Test (ORT), a speeded visual object recognition task in which participants have to find dangerous items in x-ray images of passenger bags; and in Experiment 2 (N = 155; 20–61 years) in an on-the-job object recognition test frequently used in baggage screening. Results from both experiments show high performance in older adults and significant negative age correlations that cannot be overcome by more years of job-specific experience. We discuss the implications of our findings for theories of lifespan cognitive development and training concepts.

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