Adversarially Trained Deep Neural Semantic Hashing Scheme For Subjective Search In Fashion Inventory

Abstract

The simple approach of retrieving a closest match of a query image from one in the gallery, compares an image pair using sum of absolute difference in pixel or feature space. The process is computationally expensive, ill-posed to illumination, background composition, pose variation, as well as inefficient to be deployed on gallery sets with more than 1000 elements. Hashing is a faster alternative which involves representing images in reduced dimensional simple feature spaces. Encoding images into binary hash codes enables similarity comparison in an image-pair using the Hamming distance measure. The challenge, however, lies in encoding the images using a semantic hashing scheme that lets subjective neighbors lie within the tolerable Hamming radius. This work presents a solution employing adversarial learning of a deep neural semantic hashing network for fashion inventory retrieval. It consists of a feature extracting convolutional neural network (CNN) learned to (i) minimize error in c

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keysingh2019adversarially

Related papers

• Deep Self-adaptive Hashing For Image Retrieval (2021)8.35
• Snap And Find: Deep Discrete Cross-domain Garment Image Retrieval (2019)0.00
• Deep Hashing With Semantic Hash Centers For Image Retrieval (2025)2.26
• Self-supervised Asymmetric Deep Hashing With Margin-scalable Constraint (2020)0.00
• Improved Deep Hashing With Soft Pairwise Similarity For Multi-label Image Retrieval (2018)16.82
• Unsupervised Semantic Deep Hashing (2018)10.48
• Query-adaptive Image Retrieval By Deep Weighted Hashing (2016)12.68
• SSDH: Semi-supervised Deep Hashing For Large Scale Image Retrieval (2016)15.40