Search-based Face Annotation Research Articles

Study and Analysis of Search based Face Annotation using Vision Cascade Object Detector

recognition presents a various challenges in the field of image analysis and computer vision, and large amount of work has been done over the last few years because of its applications in various domains. Mining web facial images on the internet has evolved as a promising model towards auto face annotation. Content-based image retrieval (CBIR) systems needs users to give low-level visual content images as query and then retrieve similar top matching images from database. The technique also aims to investigates framework of search-based face annotation (SBFA) by mining weakly labeled facial images that are freely available on the World Wide Web (WWW). This work presents automated face annotation that aims to automatically detect human faces from a photo and further annotate the faces with the corresponding human names. Keywordsannotation, Content based image retrieval(CBIR),Web facial images, Weak label, Unique key generation.

Mining Weakly Labelled by Search-Based Face Annotation

A face annotation has many applications the main part of based face annotation is to management of most same acial images and their weak data labels. This problem, different method is adopted. The efficiency of annotating systems are improved by using these methods. This paper proposes a review on various techniques used for detection and analysis of each technique. Combine techniques are used in retrieving facial images based on query. So it is effective to label the images with their exact names. The detected face recognition techniques can annotate the faces with exact data labels which will help to improve the detection more efficiently. For a set of semantically similar images Annotations from them. Then content-based search is performed on this set to retrieve visually similar images, annotations are mined from the data descriptions.

Mining Weakly Labeled Web Facial Images for Search-Based Face Annotation

This paper investigates a framework of search-based face annotation (SBFA) by mining weakly labeled facial images that are freely available on the World Wide Web (WWW). One challenging problem for search-based face annotation scheme is how to effectively perform annotation by exploiting the list of most similar facial images and their weak labels that are often noisy and incomplete. To tackle this problem, we propose an effective unsupervised label refinement (ULR) approach for refining the labels of web facial images using machine learning techniques. We formulate the learning problem as a convex optimization and develop effective optimization algorithms to solve the large-scale learning task efficiently. To further speed up the proposed scheme, we also propose a clustering-based approximation algorithm which can improve the scalability considerably. We have conducted an extensive set of empirical studies on a large-scale web facial image testbed, in which encouraging results showed that the proposed ULR algorithms can significantly boost the performance of the promising SBFA scheme.