Cross-batch reference learning for deep classification and retrieval, ACM MM 2016

Huei-Fang Yang, Kevin Lin, Ting-Yen Chen, and Chu-Song Chen, "Cross-batch Reference Learning for Deep Retrieval," IEEE Transactions on Neural Networks and Learning Systems, 2019

Kernelized Local Descriptors

Video retrieval from query images

MATLAB implementation of the multiple-kernel local-patch descriptor (BMVC 2017 paper)

Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations

Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations

Unsupervised Deep Hashing with Similarity-Adaptive and Discrete Optimization (TPAMI2018)

An efficient framework for image retrieval using color, texture and edge features. Implementation of a research paper. Shows similar images based on input image. Improved CBIR process is implemented in Matlab.

Matlab/Mex implementation of Aggregated Selective Match Kernels for Image Retrieval (published in ICCV 2013)

Content Based Image Retrieval Techniques (e.g. knn, svm using MatLab GUI)

Hamming Query Expansion (HQE)

Content-Based Image Retrieval (CBIR) is a challenging task which retrieves the similar images from the large database. Most of the CBIR system uses the low-level features such as color, texture and shape to extract the features from the images. In Recent years the Interest points are used to extract the most similar images with different view po…

Content-based image retrieval (CBIR) is a technique that helps in searching a user desired information from a huge set of image files and interpret user intentions for the desired information. The retrieval of information is based on features of image like colour, shape, texture, annotation etc. In addition, the system with neural network can le…

Matlab implementation of "K-Nearest Neighbors Hashing" (CVPR2019)

Code for papers "Hashing with Mutual Information" (TPAMI 2019) and "Hashing with Binary Matrix Pursuit" (ECCV 2018)

AG2E: A novel adaptive graph based multi-label learning framework for multi-label annotation, image retrieval, and other applications.

Content Based Image Retrieval.

source code for paper "Asymmetric Deep Supervised Hashing" on AAAI-2018

Graph Regularised Hashing code