Multilinear Supervised Neighborhood Embedding of a Local Descriptor Tensor for Scene/Object Recognition

2012 ◽  
Vol 21 (3) ◽  
pp. 1314-1326 ◽  
Author(s):  
Xian-Hua Han ◽  
Yen-Wei Chen ◽  
Xiang Ruan
Keyword(s):  
Object Recognition ◽  
Local Descriptor ◽  
Scene Object

scholarly journals Object recognition using hybrid boosting method

2016 ◽  
Author(s):  
Osama Ashfaq
Keyword(s):  
Object Recognition ◽  
Covariance Matrix ◽  
Local Region ◽  
Computational Time ◽  
Local Descriptor ◽  
Alternative Approach ◽  
Different Types ◽  
Boosting Method ◽  
Mixture Of Gaussian ◽  
Diagonal Covariance Matrix

Li (ICCV, 2005) proposed a novel generative/discriminative way to combine features with different types and use them to learn labels in the images. However, the mixture of Gaussian used in Li’s paper suffers greatly from the curse of dimensionality. Here I propose an alternative approach to generate local region descriptor. I treat GMM with diagonal covariance matrix and PCA as separate features, and combine them as the local descriptor. In this way, we could reduce the computational time for mixture model greatly while score greater 90% accuracies for caltech-4 image sets.

scholarly journals Selecting Discriminative Binary Patterns for a Local Feature

2015 ◽  
Vol 15 (3) ◽  
pp. 104-113
Author(s):  
Yingying Li ◽  
Jieqing Tan ◽  
Jinqin Zhong
Keyword(s):  
Object Recognition ◽  
Image Matching ◽  
State Of The Art ◽  
Local Feature ◽  
High Dimensionality ◽  
Learning Method ◽  
Local Descriptor ◽  
Local Descriptors

Abstract The local descriptors based on a binary pattern feature have state-of-the-art distinctiveness. However, their high dimensionality resists them from matching faster and being used in a low-end device. In this paper we propose an efficient and feasible learning method to select discriminative binary patterns for constructing a compact local descriptor. In the selection, a searching tree with Branch&Bound is used instead of the exhaustive enumeration, in order to avoid tremendous computation in training. New local descriptors are constructed based on the selected patterns. The efficiency of selecting binary patterns has been confirmed by the evaluation of these new local descriptors’ performance in experiments of image matching and object recognition.

scholarly journals Object recognition using hybrid boosting method

2016 ◽  
Author(s):  
Osama Ashfaq
Keyword(s):  
Object Recognition ◽  
Covariance Matrix ◽  
Local Region ◽  
Computational Time ◽  
Local Descriptor ◽  
Alternative Approach ◽  
Different Types ◽  
Boosting Method ◽  
Mixture Of Gaussian ◽  
Diagonal Covariance Matrix

Li (ICCV, 2005) proposed a novel generative/discriminative way to combine features with different types and use them to learn labels in the images. However, the mixture of Gaussian used in Li’s paper suffers greatly from the curse of dimensionality. Here I propose an alternative approach to generate local region descriptor. I treat GMM with diagonal covariance matrix and PCA as separate features, and combine them as the local descriptor. In this way, we could reduce the computational time for mixture model greatly while score greater 90% accuracies for caltech-4 image sets.

Application of Object Recognition With Shape-Index Identification and 2D Scale Invariant Feature Transform for Key-Point Detection

2018 ◽  
pp. 218-231
Author(s):  
Chiranji Lal Chowdhary
Keyword(s):  
Feature Extraction ◽  
Object Recognition ◽  
Shape Index ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Local Descriptor ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Point Detection

Humans make object recognition look inconsequential. In this chapter, scale-invariant feature extraction and shape-index depiction are used on a range of images for identifying objects. The shape-index is attained and used as a local descriptor or key-point descriptor. First surface properties for shape index identification and second as 2D scale invariant feature transformed for key-point detection and feature extraction. The object recognition classification is compared results with shape-index identification and 2D scale-invariant feature transform for key-point detection with SIFT and SURF. The authors are using images from the ImageNet dataset, and with use of shift-index + SIFT descriptors, they are finding better accuracy at the classification stage.

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