ConceptLearner: Discovering Visual Concepts from Weakly Labeled Image Collections

CVPR, June, 2015
ConceptLearner: Discovering Visual Concepts from Weakly Labeled Image Collections
Bolei Zhou, Vignesh Jagadeesh, Robinson Piramuthu
Categories
eBay Authors
Abstract
Discovering visual knowledge from weakly labeled data are crucial to scale up computer vision recognition system, since it is expensive to obtain fully labeled data for a large number of concept categories while the weakly labeled data could be collected from the Internet cheaply and massively.
 
In this paper we proposes a scalable approach to discover visual concepts from weakly labeled image collections, with thousands of visual concept detectors learned. Then we show that the learned detectors could be applied to recognize concepts at image-level and to detect concepts at image region-level accurately.
 
Under domain-selected supervision, we further evaluate the learned concepts for scene recognition on SUN database and for object detection on Pascal VOC 2007. It shows promising performance compared to the fully supervised and weakly supervised methods.
 

Another publication from the same author:

WACV, March, 2016

Fashion Apparel Detection: The Role of Deep Convolutional Neural Network and Pose-dependent Priors

Kota Hara, Vignesh Jagadeesh, Robinson Piramuthu

In this work, we propose and address a new computer vision task, which we call fashion item detection, where the aim is to detect various fashion items a person in the image is wearing or carrying. The types of fashion items we consider in this work include hat, glasses, bag, pants, shoes and so on.

The detection of fashion items can be an important first step of various e-commerce applications for fashion industry. Our method is based on state-of-the-art object detection method which combines object proposal methods with a Deep Convolutional Neural Network.

Since the locations of fashion items are in strong correlation with the locations of body joints positions, we incorporate contextual information from body poses in order to improve the detection performance. Through the experiments, we demonstrate the effectiveness of the proposed method.

Another publication from the same category: Computer Vision

Mathematics in Image Formation and Processing, July 2000

Statistical proximal point methods for image reconstruction

A.O. Hero, S. Crétien and Robinson Piramuthu