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Journal ArticleDOI

Review: Which is the best way to organize/classify images by content?

Anna Bosch, +2 more
- 01 Jun 2007 - 
- Vol. 25, Iss: 6, pp 778-791
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TLDR
A detailed review of some of the most commonly used scene classification approaches, giving the advantages and disadvantages of each methodology.
About
This article is published in Image and Vision Computing.The article was published on 2007-06-01. It has received 269 citations till now. The article focuses on the topics: Image segmentation.

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Citations
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Journal ArticleDOI

Scene Classification Using a Hybrid Generative/Discriminative Approach

TL;DR: This work introduces a novel vocabulary using dense color SIFT descriptors and investigates the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM).
Journal ArticleDOI

Real-Time Hand Gesture Detection and Recognition Using Bag-of-Features and Support Vector Machine Techniques

TL;DR: This system includes detecting and tracking bare hand in cluttered background using skin detection and hand posture contour comparison algorithm after face subtraction, recognizing hand gestures via bag-of-features and multiclass support vector machine (SVM) and building a grammar that generates gesture commands to control an application.
Journal ArticleDOI

Bag-of-Visual-Words Scene Classifier With Local and Global Features for High Spatial Resolution Remote Sensing Imagery

TL;DR: Experimental results on UC Merced and Google data sets of SIRI-WHU demonstrate that the proposed method outperforms the state-of-the-art scene classification methods for HSR imagery.
Journal ArticleDOI

Scene Classification Based on the Multifeature Fusion Probabilistic Topic Model for High Spatial Resolution Remote Sensing Imagery

TL;DR: A semantic allocation level (SAL) multifeature fusion strategy based on PTM, namely, SAL-PTM (S AL-pLSA and SAL-LDA) for HSR imagery is proposed, and the experimental results confirmed that SAL- PTM is superior to the single-feature methods and CAT-PTm in the scene classification of H SR imagery.
Book ChapterDOI

Histopathology Image Classification Using Bag of Features and Kernel Functions

TL;DR: This paper presents an evaluation of different representations obtained from the bag of features approach to classify histopathology images, and analyses the impact of each configuration in the classification result.
References
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Journal ArticleDOI

Latent dirichlet allocation

TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
Proceedings Article

Latent Dirichlet Allocation

TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).
Journal ArticleDOI

A feature-integration theory of attention

TL;DR: A new hypothesis about the role of focused attention is proposed, which offers a new set of criteria for distinguishing separable from integral features and a new rationale for predicting which tasks will show attention limits and which will not.
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.
Journal ArticleDOI

Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope

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.
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