Metric learning via feature weighting for scalable image retrieval

Lv, Xiaoming; Duan, Fajie

doi:10.1016/j.patrec.2017.09.026

Cited by 9 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The poses and lighting of the flowers in the images vary. On this dataset, many multimodal tasks including image generation tasks [16,38], text generation tasks [35,39] and retrieval tasks [36,40] are benchmarked and compared with each other.…”

Section: Introductionmentioning

confidence: 99%

GSAIC: GeoScience Articles Illustration and Caption Dataset

Shi¹

2022

HSET

View full text Add to dashboard Cite

The scientific investigation of geoscience includes data collection, sample classification and semantic, consisting of a large number of images. An image-text search model that can well assist the research work of geoscience. However, the existing image-text datasets are mainly in the field of daily life and lack academic image-text datasets. In order to help geoscience researchers to investigate through the image and text, and to provide a new benchmark for researchers in the fields of data mining and information retrieval, this paper proposes a novel parallel material of geoscience academic illiustrateion and caption (GSAIC) based on GAKG, which contains over 900,000 illustrations of earth science papers and the corresponding captions. GSAIC filters out high-quality illustrations and captions through a classifier, and with the support of experts annotations. The GSAIC will support several tasks Including text search for images, retrieving corresponding images or papers based on academic image descriptions and academic illustration classification tasks, for geoscience scenarios Finally, both the GSAIC benchmark and classifier are publicly accessible.

show abstract

Section: Introductionmentioning

confidence: 99%

GSAIC: GeoScience Articles Illustration and Caption Dataset

Shi¹

2022

HSET

View full text Add to dashboard Cite

show abstract

“…There is an increasing number of researchers contribute their great endeavours on large-scale image or video retrieval due to its wide practical utilities (Bakhtiary et al, 2017;Zhang et al, 2017b;Chen et al, 2016;Lv et al, 2017;Zhang et al, 2018Zhang et al, , 2017c. Hashing is a powerful and well-known large-scale image retrieval technique, which encode the real-valued image into binary codes, such as '0/1' or '-1/+1'.…”

Section: Introductionmentioning

confidence: 99%

Zero-shot Hashing with orthogonal projection for image retrieval

Zhang

Long

Shao

2019

Pattern Recognition Letters

View full text Add to dashboard Cite

Hashing has been widely used in large-scale image retrieval. Supervised information such as semantic similarity and class label, and Convolutional Neural Network (CNN) has greatly improved the quality of hash codes and hash functions. However, due to the explosive growth of web data, existing hashing methods can not well perform on emerging images of new classes. In this paper, we propose a novel hashing method based on orthogonal projection of both image and semantic attribute, which constrains the generated binary codes in orthogonal space should be orthogonal with each other when they belong to different classes, otherwise be same. This constraint guarantees that the generated hash codes from different categories have equal Hamming distance, which also makes the space more discriminative within limited code length. To improve the performance, we also extend our method with a deep model. Experiments of both our linear and deep model on three popular datasets show that our method can achieve competitive results, specially, the deep model can outperform all the listed state-of-the-art approaches.

show abstract