In this paper, we investigate how to establish the relationship between semantic concepts based on the large-scale realworld click data from image commercial engine, which is a challenging topic because the click data suffers from the noise such as typos, the same concept with different queries, etc. We first define five specific relationships between concepts. We then extract some concept relationship features in textual and visual domain to train the concept relationship models. The relationship of each pair of concepts will thus be classified into one of the five special relationships. We study the efficacy of the conceptual relationships by applying them to augment imperfect image tags, i.e., improve representative power. We further employ a sophisticated hashing approach to transform augmented image tags into binary codes, which are subsequently used for content-based image retrieval task. Experimental results on NUS-WIDE dataset demonstrate the superiority of our proposed approach as compared to state-of-the-art methods.
Discovering visual knowledge from weakly labeled data is crucial to scale up computer vision recognition systems, since it is expensive to obtain fully labeled data for a large ...
We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Crea...
This paper proposes two novel image similarity measures for fast indexing via locality sensitive hashing. The similarity measures are applied and evaluated in the context of nea...
Clustering Web 2.0 items (i.e., web resources like videos, images) into semantic groups benefits many applications, such as organizing items, generating meaningful tags and impr...
While convolutional neural networks have gained impressive success recently in solving structured prediction problems such as semantic segmentation, it remains a challenge to di...
Social media, news, blog, policy document mentions