18 research outputs found
Clothing Co-Parsing by Joint Image Segmentation and Labeling
This paper aims at developing an integrated system of clothing co-parsing, in
order to jointly parse a set of clothing images (unsegmented but annotated with
tags) into semantic configurations. We propose a data-driven framework
consisting of two phases of inference. The first phase, referred as "image
co-segmentation", iterates to extract consistent regions on images and jointly
refines the regions over all images by employing the exemplar-SVM (E-SVM)
technique [23]. In the second phase (i.e. "region co-labeling"), we construct a
multi-image graphical model by taking the segmented regions as vertices, and
incorporate several contexts of clothing configuration (e.g., item location and
mutual interactions). The joint label assignment can be solved using the
efficient Graph Cuts algorithm. In addition to evaluate our framework on the
Fashionista dataset [30], we construct a dataset called CCP consisting of 2098
high-resolution street fashion photos to demonstrate the performance of our
system. We achieve 90.29% / 88.23% segmentation accuracy and 65.52% / 63.89%
recognition rate on the Fashionista and the CCP datasets, respectively, which
are superior compared with state-of-the-art methods.Comment: 8 pages, 5 figures, CVPR 201
Implementation of Convolutional Neural Network Method in Identifying Fashion Image
The fashion industry has changed a lot over the years, which makes it hard for people to compare different kinds of fashion. To make it easier, different styles of clothing are tried out to find the exact and precise look desired. So, we opted to employ the Convolutional Neural Network (CNN) method for fashion classification. This approach represents one of the methodologies employed to utilize computers for the purpose of recognizing and categorizing items. The goal of this research is to see how well the Convolutional Neural Network method classifies the Fashion-MNIST dataset compared to other methods, models, and classification processes used in previous research. The information in this dataset is about different types of clothes and accessories. These items are divided into 10 categories, which include ankle boots, bags, coats, dresses, pullovers, sandals, shirts, sneakers, t-shirts, and trousers. The new classification method worked better than before on the test dataset. It had an accuracy value of 95. 92%, which is higher than in previous research. This research also uses a method called image data generator to make the Fashion MNIST image better. This method helps prevent too much focus on certain details and makes the results more accurate
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Sampling and Learning of the And-Or Graph
The And-Or graph is a tool for knowledge representation. In this thesis we first study thesampling of the And-Or graph with or without context constraints. Without any constrainton the potential functions of the And-Or graph nodes, the positions and shapes of differ-ent components of the face images are not aligned properly. In contrast, with both unaryconstraints and binary constraints, the components are aligned and the samples are morerepresentative of the And-Or graph. We further explore parameter and structure learning ofthe And-Or graph by implementing and applying some existing algorithms. The experimen-tal results on 1D text data and 2D face image data are shown. While there is no apparentdifference between the sampling results of the parameter learned And-Or graph and the trueAnd-Or graph, the sampling results of the structure learned And-Or graph are not perfectand could be further improved
A Latent Clothing Attribute Approach for Human Pose Estimation
As a fundamental technique that concerns several vision tasks such as image
parsing, action recognition and clothing retrieval, human pose estimation (HPE)
has been extensively investigated in recent years. To achieve accurate and
reliable estimation of the human pose, it is well-recognized that the clothing
attributes are useful and should be utilized properly. Most previous
approaches, however, require to manually annotate the clothing attributes and
are therefore very costly. In this paper, we shall propose and explore a
\emph{latent} clothing attribute approach for HPE. Unlike previous approaches,
our approach models the clothing attributes as latent variables and thus
requires no explicit labeling for the clothing attributes. The inference of the
latent variables are accomplished by utilizing the framework of latent
structured support vector machines (LSSVM). We employ the strategy of
\emph{alternating direction} to train the LSSVM model: In each iteration, one
kind of variables (e.g., human pose or clothing attribute) are fixed and the
others are optimized. Our extensive experiments on two real-world benchmarks
show the state-of-the-art performance of our proposed approach.Comment: accepted to ACCV 2014, preceding work http://arxiv.org/abs/1404.492