DeepAI AI Chat
Log In Sign Up

Supervised Incremental Hashing

by   Bahadir Ozdemir, et al.

We propose an incremental strategy for learning hash functions with kernels for large-scale image search. Our method is based on a two-stage classification framework that treats binary codes as intermediate variables between the feature space and the semantic space. In the first stage of classification, binary codes are considered as class labels by a set of binary SVMs; each corresponds to one bit. In the second stage, binary codes become the input space of a multi-class SVM. Hash functions are learned by an efficient algorithm where the NP-hard problem of finding optimal binary codes is solved via cyclic coordinate descent and SVMs are trained in a parallelized incremental manner. For modifications like adding images from a previously unseen class, we describe an incremental procedure for effective and efficient updates to the previous hash functions. Experiments on three large-scale image datasets demonstrate the effectiveness of the proposed hashing method, Supervised Incremental Hashing (SIH), over the state-of-the-art supervised hashing methods.


page 6

page 11

page 12

page 13


Scalable Gaussian Processes for Supervised Hashing

We propose a flexible procedure for large-scale image search by hash fun...

ElasticHash: Semantic Image Similarity Search by Deep Hashing with Elasticsearch

We present ElasticHash, a novel approach for high-quality, efficient, an...

Supervised Learning of Semantics-Preserving Hash via Deep Convolutional Neural Networks

This paper presents a simple yet effective supervised deep hash approach...

Learning Binary Codes and Binary Weights for Efficient Classification

This paper proposes a generic formulation that significantly expedites t...

A non-alternating graph hashing algorithm for large scale image search

In the era of big data, methods for improving memory and computational e...

Hashed Binary Search Sampling for Convolutional Network Training with Large Overhead Image Patches

Very large overhead imagery associated with ground truth maps has the po...

HashEncoding: Autoencoding with Multiscale Coordinate Hashing

We present HashEncoding, a novel autoencoding architecture that leverage...