User-Aware Image Tag Refinement via Ternary Semantic Analysis

Structured fragment-based object tracking using discrimination, uniqueness, and validity selection

“…For most of the recently reported algorithms [48][49][50][51][52][53][54][55][56][57][58][59]58], in contrast, spatial constraints between these fragments are often imposed, and these algorithms are more robust to deformation and illumination changes. Among these algorithms, graph representation-based method is a particularly popular method, which include inter-ARG and intra-ARG [51], attributed relational feature graph (ARFG) [52], dynamic graph(DG) [56], memory graph [57], star model [48], etc.…”

Personalized search for social media via dominating verbal context

“…The tags depict various aspects from the movie intrinsic content to user extrinsic perception. Employing the criteria of the inactive user described in [24], we select users who have tagged no less than 15 resources and exclude inactive users.…”

Projective nonnegative matrix factorization for social image retrieval

“…We introduce an iterative procedure to optimize (18) and present the details in Algorithm 1. The proposed optimization algorithm is convergent, which can be proved by following the strategy in [41].…”

“…They are mostly train on manually labeled data and tested on small data sets [13], which make them unsuitable for social image tagging. In the second scenario, given an image labeled with some tags, tag relevance learning can be used to remove noisy tags, recommend new relevant tags or reduce tag ambiguity [2,[14][15][16][17][18][19]. Tag ranking [15] exploits pairwise similarity between tags by random walk to refine the ranking score.…”