Mine the Fine: Fine-Grained Fragment Discovery

ICIP, September, 2015
Mine the Fine: Fine-Grained Fragment Discovery
M. Hadi Kiapour, Wei Di, Vignesh Jagadeesh, Robinson Piramuthu
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Abstract

While discriminative visual element mining has been introduced before, in this paper we present an approach that requires minimal annotation in both training and test time. Given only a bounding box localization of the foreground objects, our approach automatically transforms the input images into a roughly-aligned pose space and discovers the most discriminative visual fragments for each category.

These fragments are then used to learn robust classifiers that discriminate between very similar categories under challenging conditions such as large variations in pose or habitats. The minimal required input, is a critical characteristic that enables our approach to generalize over visual domains where expert knowledge is not readily available.

Moreover, our approach takes advantage of deep networks that are targeted towards fine-grained classification.It learns mid-level representations that are specific to a category and generalize well across the category instances at the same time.

Our evaluations demonstrate that the automatically learned representation based on discriminative fragments, significantly outperforms globally extracted deep features in classification accuracy.

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: Trust Science

2015 International Conference for Machine Learning (ICML)

Bayesian and Empirical Bayesian Forests

Matt Taddy, Chun-Sheng Chen, Jun Yu, Mitch Wyle

We derive ensembles of decision trees through a nonparametric Bayesian model, allowing us to view random forests as samples from a posterior distribution. This insight provides large gains in interpretability, and motivates a class of Bayesian forest (BF) algorithms that yield small but reliable performance gains.

Based on the BF framework, we are able to show that high-level tree hierarchy is stable in large samples. This leads to an empirical Bayesian forest (EBF) algorithm for building approximate BFs on massive distributed datasets and we show that EBFs outperform subsampling based alternatives by a large margin.

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