Image collection summarization via dictionary learning for sparse representation

Scalable gastroscopic video summarization via similar-inhibition dictionary selection

“…Liu et al [24] model the summarization procedure as a maximum a posterior problem by integrating both shot boundary detection and key frame selection. In [2], the key frames are selected via a novel dictionary selection model, and similar ideas are also applied in [25][26][27][28].…”

Topic aspect-oriented summarization via group selection

“…Hence, we choose some unsupervised methods to compared with, including AP (Affinity Propagation) [22], K-medoids [6] and DL (Dictionary Learning) [3].…”

“…Many extraction-based summarization methods have been proposed in the past years. In the unsupervised setting, LSI (Latent Semantic Indexing) [1] and NMF (Non-negative Matrix Factorization) [2] are widely implemented in text mining; Dictionary Learning [3][4][5] is used in the processing of cross-media data. Moreover, a variant of K-means, named K-medoids [6] is used in the summarization of image data.…”

Sparse discriminative feature selection

“…Recently, the theory of sparse representation has been successfully integrated with compressed sensing [15,16], image analysis [17][18][19], and dimension reduction [20][21][22][23][24]. All these sparse representation based dimension reduction methods borrow the idea of sparse representation classification (SRC) [25], and they are essentially feature extraction methods.…”