Sorry, you need to enable JavaScript to visit this website.

USING DEEP CROSS MODAL HASHING AND ERROR CORRECTING CODES FOR IMPROVING THE EFFICIENCY OF ATTRIBUTE GUIDED FACIAL IMAGE RETRIEVAL

Citation Author(s):
Veeru Talreja, Fariborz Taherkhani, Matthew C. Valenti, Nasser M. Nasrabadi
Submitted by:
Veeru Talreja
Last updated:
24 November 2018 - 12:05am
Document Type:
Presentation Slides
Document Year:
2018
Event:
Presenters:
Veeru Talreja
Paper Code:
1478
 

With benefits of fast query speed and low storage cost,hashing-based image retrieval approaches have garnered considerable attention from the research community. In this pa-per, we propose a novel Error-Corrected Deep Cross Modal Hashing (CMH-ECC) method which uses a bitmap specifying the presence of certain facial attributes as an input query to retrieve relevant face images from the database. In this architecture, we generate compact hash codes using an end-to-end deep learning module, which effectively captures the inherent relationships between the face and attribute modality. We also integrate our deep learning module with forward error correction codes to further reduce the distance between different modalities of the same subject. Specifically, the properties of deep hashing and forward error correction codes are exploited to design a cross modal hashing framework with high retrieval performance. Experimental results using two standard datasets with facial attributes-image modalities indicate that our CMH-ECC face image retrieval model outperforms most of the current attribute-based face image retrieval approaches

up
0 users have voted: