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

By Anna Bosch, Xavier Muñoz, Robert Marti. Image and Vision Computing, 2007

297 citations recorded by the source corpus.

How researchers see this paper

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Scene Classification Using a Hybrid Generative/Discriminative Approach (2008, background): “As was noted in [3], two basic strategies can be found in the literature.”
A 2-D wavelet decomposition-based bag-of-visual-words model for land-use scene classification (2014, background): “…describes it with local descriptors (Mikolajczyk and Schmid 2005) among which affine invariant features (e.g. the scale-invariant feature transform (SIFT) (Lowe 2004)) derived from image patches have become the most successful image representations (Bosch, Muñoz, and Martí 2007; Zhang et al. 2007).”
Scene classification based on single-layer SAE and SVM (2015, background): “Feature extraction plays an important role in scene classification problems (Bosch et al., 2007; Serrano-Talamantes et al., 2013).”
Real-Time Hand Gesture Detection and Recognition Using Bag-of-Features and Support Vector Machine Techniques (2011, unknown): “The next step is using the vector quantization (VQ) technique [3], which clusters the keypoint descriptors in their feature space into a large number of clusters using the K-means clustering algorithm and encodes each keypoint by the index of the cluster (codevector) to which it belongs.”
Integrated visual vocabulary in latent Dirichlet allocation–based scene classification for IKONOS image (2014, background): “, (1) over the combined GLCM features and color moments, and (2) over GLCM features and color moments separately.”