In the early days, content-based image retrieval (CBIR) was studied with global features.
Since 2003, image retrieval based on local descriptors (de facto SIFT) has been extensively …

The explosive increase and ubiquitous accessibility of visual data on the Web have led to
the prosperity of research activity in image search or retrieval. With the ignorance of visual …

The lack of annotated publicly available medical images is a major barrier for computational
research and education innovations. At the same time, many de-identified images and much …

Over the past decades, medical image analytics was greatly facilitated by the explosion of
digital imaging techniques, where huge amounts of medical images were produced with …

Feature fusion has been proven effective [31, 32] in image search. Typically, it is assumed
that the to-be-fused heterogeneous features work well by themselves for the query …

Person reidentification is a key technique to match different persons observed in
nonoverlapping camera views. Many researchers treat it as a special object-retrieval …

Query expansion is a popular method to improve the quality of image retrieval with both
conventional and CNN representations. It has been so far limited to global image similarity …

Visual content analysis has always been important yet challenging. Thanks to the popularity
of social networks, images become an convenient carrier for information diffusion among …

The objective of this paper is the effective transfer of the Convolutional Neural Network
(CNN) feature in image search and classification. Systematically, we study three facts in …

Abstract In Bag-of-Words (BoW) based image retrieval, the SIFT visual word has a low
discriminative power, so false positive matches occur prevalently. Apart from the information …