English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The existing image feature extraction methods are primarily based on thecontent and structure information of images, and rarely consider the contextualsemantic information. Regarding some types of images such as scenes andobjects, the annotations and descriptions of them available on the web mayprovide reliable contextual semantic information for feature extraction. Inthis paper, we introduce novel semantic features of an image based on theannotations and descriptions of its similar images available on the web.Specifically, we propose a new method which consists of two consecutive stepsto extract our semantic features. For each image in the training set, weinitially search the top $k$ most similar images from the internet and extracttheir annotations/descriptions (e.g., tags or keywords). The annotationinformation is employed to design a filter bank for each image category andgenerate filter words (codebook). Finally, each image is represented by thehistogram of the occurrences of filter words in all categories. We evaluate theperformance of the proposed features in scene image classification on threecommonly-used scene image datasets (i.e., MIT-67, Scene15 and Event8). Ourmethod typically produces a lower feature dimension than existing featureextraction methods. Experimental results show that the proposed featuresgenerate better classification accuracies than vision based and tag basedfeatures, and comparable results to deep learning based features.

Tag-based Semantic Features for Scene Image Classification