Learning Image Conditioned Label Space for Multilabel Classification

02/21/2018
by   Yi-Nan Li, et al.
0

This work addresses the task of multilabel image classification. Inspired by the great success from deep convolutional neural networks (CNNs) for single-label visual-semantic embedding, we exploit extending these models for multilabel images. Specifically, we propose an image-dependent ranking model, which returns a ranked list of labels according to its relevance to the input image. In contrast to conventional CNN models that learn an image representation (i.e. the image embedding vector), the developed model learns a mapping (i.e. a transformation matrix) from an image in an attempt to differentiate between its relevant and irrelevant labels. Despite the conceptual simplicity of our approach, experimental results on a public benchmark dataset demonstrate that the proposed model achieves state-of-the-art performance while using fewer training images than other multilabel classification methods.

READ FULL TEXT
research
04/15/2016

CNN-RNN: A Unified Framework for Multi-label Image Classification

While deep convolutional neural networks (CNNs) have shown a great succe...
research
06/28/2017

Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning

The Classification of medical images and illustrations in the literature...
research
12/04/2016

Multi-Label Image Classification with Regional Latent Semantic Dependencies

Deep convolution neural networks (CNN) have demonstrated advanced perfor...
research
07/23/2020

Zero-Shot Recognition through Image-Guided Semantic Classification

We present a new embedding-based framework for zero-shot learning (ZSL)....
research
10/28/2020

CNN Profiler on Polar Coordinate Images for Tropical Cyclone Structure Analysis

Convolutional neural networks (CNN) have achieved great success in analy...
research
06/04/2021

Efficient Classification of Very Large Images with Tiny Objects

An increasing number of applications in the computer vision domain, spec...
research
05/30/2018

Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images

Automated dermoscopic image analysis has witnessed rapid growth in diagn...

Please sign up or login with your details

Forgot password? Click here to reset