Style Finder: Fine-Grained Clothing Style Recognition and Retrieval

To appear in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Mobile Vision Workshop, 2013.
Style Finder: Fine-Grained Clothing Style Recognition and Retrieval
Wei Di, Catherine Wah, Anurag Bhardwaj, Robinson Piramuthu, Neel Sundaresan
eBay Authors

With the rapid proliferation of smartphones and tablet computers, search has moved beyond text to other modalities like images and voice. For many applications like Fashion, visual search offers a compelling interface that can capture stylistic visual elements beyond color and pattern that cannot be as easily described using text.

However, extracting and matching such attributes remains an extremely challenging task due to high variability and deformability of clothing items. In this paper, we propose a fine-grained learning model and multimedia retrieval framework to address this problem.

First, an attribute vocabulary is constructed using human annotations obtained on a novel fine-grained clothing dataset. This vocabulary is then used to train a fine-grained visual recognition system for clothing styles.

We report benchmark recognition and retrieval results on Women's Fashion Coat Dataset and illustrate potential mobile applications for attribute-based multimedia retrieval of clothing items and image annotation.

Another publication from the same author: Robinson Piramuthu

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