We describe a completely automated large scale visual recommendation system for fashion. Existing approaches have primarily relied on purely computational models to solving this problem that ignore the role of users in the system.
In this paper, we propose to overcome this limitation by incorporating a user-centric design of visual fashion recommendations. Specifically, we propose a technique that augments 'user preferences' in models by exploiting elasticity in fashion choices. We further design a user study on these choices and gather results from the 'wisdom of crowd' for deeper analysis.
Our key insights learnt through these results suggest that fashion preferences when constrained to a particular class, contain important behavioral signals that are often ignored in recommendation design.
Further, presence of such classes also reflect strong correlations to visual perception which can be utilized to provide aesthetically pleasing user experiences. Finally, we illustrate that user approval of visual fashion recommendations can be substantially improved by carefully incorporating these user-centric feedback into the system framework.
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.
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.
xplosion of mobile devices with cameras, online search has moved beyond text to other modalities like images, voice, and writing. For many applications like Fashion, image-based search offers a compelling interface as compared to text forms by better capturing the visual attributes.
In this paper we present a simple and fast search algorithm that uses color as the main feature for building visual search. We show that low level cues such as color can be used to quantify image similarity and also to discriminate among products with different visual appearances.
We demonstrate the effectiveness of our approach through a mobile shopping application (eBay Fashion App available at https://itunes.apple.com/us/app/ebay-fashion/id378358380?mt=8 and eBay image swatch is the feature indexing millions of real world fashion images).
Our approach outperforms several other state-of-the-art image retrieval algorithms for large scale image data.