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Deep Feature Factorization For Content-Based Image Retrieval And Localization

Publications associées (40)

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Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation

Mathieu Salzmann, Fatemehsadat Saleh

Pixel-level annotations are expensive and time consuming to obtain. Hence, weak supervision using only image tags could have a significant impact in semantic segmentation. Recently, CNN-based methods have proposed to fine-tune pre-trained networks using im ...

Springer Int Publishing Ag2016

We are interested in inferring object segmentation by leveraging only object class information, and by considering only minimal priors on the object segmentation task. This problem could be viewed as a kind of weakly supervised segmentation task, and natur ...

2015

Adaptive Relevance Feedback for Large-scale Image Retrieval

Nicolae Suditu

Our research addresses the need for an efficient, effective, and interactive access to large-scale image collections. Image retrieval needs are evolving beyond the capabilities of the traditional indexing based on manual annotation, and the most desirable ...

EPFL2013

Content Based Image Retrieval (CBIR) has gained a lot of interest over the last two decades. The need to search and retrieve images from databases, based on information (“features”) extracted from the image itself, is becoming increasingly important. CBIR ...

EPFL2013

Iterative EEG-based Natural Image Search under RSVP

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Marija Uscumlic

This work extends previous studies on using EEG decoding for automatic image retrieval. We propose an iterative way to integrate the information obtained from the EEG decoding and image processing methods. In the light of real-world BCI applications, we de ...

Graz University of Technology Publishing House2013

Joint Image and Word Sense Discrimination For Image Retrieval

Aurélien Lucchi

We study the task of learning to rank images given a text query, a problem that is complicated by the issue of multiple senses. That is, the senses of interest are typically the visually distinct concepts that a user wishes to retrieve. In this paper, we p ...

2012

Iterative Relevance Feedback with Adaptive Exploration/Exploitation Trade-off

François Fleuret, Nicolae Suditu

Content-based image retrieval systems have to cope with two different regimes: understanding broadly the categories of interest to the user, and refining the search in this or these categories to converge to specific images among them. Here, in contrast wi ...

2012

Image Retrieval in the Plenoptic Space

Alireza Ghasemi

In this report we study the ways to exploit the vast amount of information inherent in the plenoptic space and constraints of the plenoptic function to improve the efficiency of image retrieval, recognition and matching techniques. The plenoptic space is f ...

2012

It has been shown repeatedly that iterative relevance feedback is a very efficient solution for content-based image retrieval. However, no existing system scales gracefully to hundreds of thousands or millions of images. We present a new approach dubbed Hi ...

2011

HEAT: Iterative Relevance Feedback with One Million Images

François Fleuret, Nicolae Suditu

Idiap2011