Journal ArticleDOI
Image classification using spatial pyramid robust sparse coding
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TLDR
A new image classification method by spatial pyramid robust sparse coding (SP-RSC), which tries to find the maximum likelihood estimation solution by alternatively optimizing over the codebook and local feature coding parameters, hence is more robust to outliers than traditional sparse coding based methods.About:
This article is published in Pattern Recognition Letters.The article was published on 2013-07-01. It has received 43 citations till now. The article focuses on the topics: Sparse approximation & Neural coding.read more
Citations
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Journal ArticleDOI
Fine-Grained Image Classification via Low-Rank Sparse Coding With General and Class-Specific Codebooks
TL;DR: This paper tries to separate fine-grained images by jointly learning the encoding parameters and codebooks through low-rank sparse coding (LRSC) with general and class-specific codebook generation by jointly encoding the local features within a spatial region jointly by LRSC.
Journal ArticleDOI
Unsupervised and Semi-Supervised Image Classification With Weak Semantic Consistency
Chunjie Zhang,Jian Cheng,Qi Tian +2 more
TL;DR: A novel weak semantic consistency constrained image classification method that starts from an extreme circumstance by viewing each image as one class and conducts both unsupervised and semi-supervised experiments on several datasets.
Journal ArticleDOI
Multi-View Image Classification With Visual, Semantic and View Consistency
Chunjie Zhang,Jian Cheng,Qi Tian +2 more
TL;DR: This paper proposes a novel multi-view image classification method with visual, semantic and view consistency (VSVC), and performs image classification experiments on several public datasets to evaluate the effectiveness.
Journal ArticleDOI
Saliency-driven image classification method based on histogram mining and image score
TL;DR: A new saliency-driven bag-of-phrase approach for image classification where saliency map and local features are first extracted from edge-based dense descriptors and represented by histogram and mined with discriminative learning technique.
Journal ArticleDOI
Few-Shot Visual Classification Using Image Pairs With Binary Transformation
TL;DR: A novel visual classification method using image pairs with binary transformation (IPBT) to classify images using few-shot samples by concatenating the representations of the two images along with their similarity.
References
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Journal ArticleDOI
Robust Face Recognition via Sparse Representation
TL;DR: This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.
Proceedings ArticleDOI
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
TL;DR: This paper presents a method for recognizing scene categories based on approximate global geometric correspondence that exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories.
Proceedings ArticleDOI
Video Google: a text retrieval approach to object matching in videos
TL;DR: An approach to object and scene retrieval which searches for and localizes all the occurrences of a user outlined object in a video, represented by a set of viewpoint invariant region descriptors so that recognition can proceed successfully despite changes in viewpoint, illumination and partial occlusion.
Journal ArticleDOI
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
Aude Oliva,Antonio Torralba +1 more
TL;DR: The performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category.
Proceedings ArticleDOI
A Bayesian hierarchical model for learning natural scene categories
Li Fei-Fei,Pietro Perona +1 more
TL;DR: This work proposes a novel approach to learn and recognize natural scene categories by representing the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning.