Jingchen Liu, Thommen Korah, Varsha Hedau, Vasu Parameswaran, Radek Grzeszczuk, Yanxi Liu
SUNw: Scene Understanding Workshop, CVPR 2014
Categories: Vision
Zhicheng Yan, Vignesh Jagadeesh, Dennis DeCoste, Wei Di, Robinson Piramuthu
arXiv, October, 2014
Categories: Machine Learning, Vision
Rohit Pandey, Wei Di, Vignesh Jagadeesh, Robinson Piramuthu, Anurag Bhardwaj
IEEE International Conference on Image Processing (ICIP), 2014
Abstract [+]
We present a new feature representation method for scene text recognition problem, particularly focusing on improving scene character recognition. Many existing methods rely on Histogram of Oriented Gradient (HOG) or part-based models, which do not span the feature space well for characters in natural scene images, especially given large variation in fonts with cluttered backgrounds. In this work, we propose a discriminative feature pooling method that automatically learns the most informative sub-regions of each scene character within a multi-class classification framework, whereas each sub-region seamlessly integrates a set of low-level image features through integral images. The proposed feature representation is compact, computationally efficient, and able to effectively model distinctive spatial structures of each individual character class. Extensive experiments conducted on challenging datasets (Chars74K, ICDAR’03, ICDAR’11, SVT) show that our method significantly outperforms existing methods on scene character classification and scene text recognition tasks.
Categories: Vision
Zixuan Wang, Wei Di, Anurag Bhardwaj, Vignesh Jagadeesh, Robinson Piramuthu
ICML 2014 workshop on New Learning Models and Frameworks for BigData
Abstract [+]
We present a novel compact image descriptor for large scale image search. Our proposed descriptor - Geometric VLAD (gVLAD) is an extension of VLAD (Vector of locally Aggregated Descriptors) that incorporates weak geometry information into the VLAD framework. The proposed geometry cues are derived as a membership function over keypoint angles which contain evident and informative information but yet often discarded. A principled technique for learning the membership function by clustering angles is also presented. Further, to address the overhead of iterative codebook training over real-time datasets, a novel codebook adaptation strategy is outlined. Finally, we demonstrate the efficacy of proposed gVLAD based retrieval framework where we achieve more than 15% improvement in mAP over existing benchmarks.
Categories: Vision
Chen YuLee, Anurag Bhardwaj, Wei Di, Vignesh Jagadeesh, Robinson Piramuthu
CVPR 2014
Abstract [+]
We present a new feature representation method for scene text recognition problem, particularly focusing on improving scene character recognition. Many existing methods rely on Histogram of Oriented Gradient (HOG) or part based models, which do not span the feature space well for characters in natural scene images, especially given large variation in fonts with cluttered backgrounds. In this work, we propose a discriminative feature pooling method that automatically learns the most informative sub-regions of each scene character within a multi-class classification framework, whereas each sub-region seamlessly integrates a set of low-level image features through integral images. The proposed feature representation is compact, computationally efficient, and able to effectively model distinctive spatial structures of each individual character class. Extensive experiments conducted on challenging datasets (Chars74K, ICDAR’03, ICDAR’11, SVT) show that our method significantly outperforms existing methods on scene character classification and scene text recognition tasks.
Categories: Vision
Vignesh Jagadeesh, Robinson Piramuthu, Anurag Bhardwaj, Wei Di, Neel Sundaresan
KDD 2014
Abstract [+]
We describe a completely automated large scale visual recommendation system for fashion. Our focus is to efficiently harness the availability of large quantities of online fashion images and their rich meta-data. Specifically, we propose two classes of data driven models in the Deterministic Fashion Recommenders (DFR) and Stochastic Fashion Recommenders (SFR) for solving this problem. We analyze relative merits and pitfalls of these algorithms through extensive experimentation on a large-scale data set and baseline them against existing ideas from color science. We also illustrate key fashion insights learned through these experiments and show how they can be employed to design better recommendation systems. The industrial applicability of proposed models is in the context of mobile fashion shopping. Finally, we also outline a largescale annotated data set of fashion images (Fashion-136K) that can be exploited for future research in data driven visual fashion.
Categories: Vision
Wei Di, Neel Sundaresan, Robinson Piramuthu, Anurag Bhardwaj
Abstract [+]
In online peer-to-peer commerce places where physical examination of the goods is infeasible, textual descriptions, images of the products, reputation of the participants, play key roles. Visual image is a powerful channel to convey crucial information towards e-shoppers and influence their choice. In this paper, we investigate a well-known online marketplace where over millions of products change hands and most are described with the help of one or more images. We present a systematic data mining and knowledge discovery approach that aims to quantitatively dissect the role of images in e-commerce in great detail. Our goal is two-fold. First, we aim to get a thorough understanding of impact of images across various dimensions: product categories, user segments, conversion rate. We present quantitative evaluation of the influence of images and show how to leverage different image aspects, such as quantity and quality, to effectively raise sale. Second, we study interaction of image data with other selling dimensions by jointly modeling them with user behavior data. Results suggest that "watch" behavior encodes complex signals combining both attention and hesitation from buyer, in which image still holds an important role when compared to other selling variables, especially for products for which appearance is important. We conclude on how these findings can benefit sellers in a high competitive online e-commerce market.
Categories: Vision
Zitao Liu, Gyanit Singh, Nish Parikh, Neel Sundaresan
In proceedings of the Workshop on Log-based Personalization (the 4th WSCD workshop) at WSDM 2014.
Abstract [+]
Personalization off ers the promise of improving online search and shopping experience. In this work, we perform a large scale analysis on the sample of eBay query logs, which involves 9.24 billion session data spanning 12 months (08/2012-07/2013) and address the following topics (1) What user information is useful for personalization; (2) Importance of per-query personalization (3) Importance of recency in query prediction. In this paper, we study these problems and provide some preliminary conclusions.
Darrell Hoy, Elizabeth Churchill, Atish Das Sarma, Kamal Jain
CHIMoney (Workshop at CHI-2014)
Categories: Human Computer Interaction
Vicente Ordonez, Vignesh Jagadeesh, Wei Di, Anurag Bhardwaj, Robinson Piramuthu
WACV 2014: IEEE Winter Conference on Applications of Computer Vision
Abstract [+]
As the amount of user generated content on the internet grows, it becomes ever more important to come up with vision systems that learn directly from weakly annotated and noisy data. We leverage a large scale collection of user generated content comprising of images, tags and title/captions of furniture inventory from an e-commerce website to discover and categorize learnable visual attributes. Furniture categories have long been the quintessential example of why computer vision is hard, and we make one of the first attempts to understand them through a large scale weakly annotated dataset. We focus on a handful of furniture categories that are associated with a large number of fine-grained attributes. We propose a set of localized feature representations built on top of state-of-the-art computer vision representations originally designed for fine-grained object categorization. We report a thorough empirical characterization on the visual identifiability of various fine-grained attributes using these representations and show encouraging results on finding iconic images and on multi-attribute prediction.
Categories: Vision