20 citations · 40 across the 46 of their papers we have counts for
6 papers · 1 filter
Finite State Machines for Semantic Scene Parsing and Segmentation
Hichem Sahbi
We introduce in this work a novel stochastic inference process, for scene annotation and object class segmentation, based on finite state machines (FSMs). The design principle of o…
Canonical Correlation Analysis for Misaligned Satellite Image Change Detection
Hichem Sahbi
Canonical correlation analysis (CCA) is a statistical learning method that seeks to build view-independent latent representations from multi-view data. This method has been success…
Cascaded Coarse-to-Fine Deep Kernel Networks for Efficient Satellite Image Change Detection
Hichem Sahbi
Deep networks are nowadays becoming popular in many computer vision and pattern recognition tasks. Among these networks, deep kernels are particularly interesting and effective, ho…
Learning Explicit Deep Representations from Deep Kernel Networks
Mingyuan Jiu, Hichem Sahbi
Deep kernel learning aims at designing nonlinear combinations of multiple standard elementary kernels by training deep networks. This scheme has proven to be effective, but intract…
Learning Deep Context-Network Architectures for Image Annotation
Mingyuan Jiu, Hichem Sahbi
Context plays an important role in visual pattern recognition as it provides complementary clues for different learning tasks including image classification and annotation. In the…
Graph Kernels based on High Order Graphlet Parsing and Hashing
Anjan Dutta, Hichem Sahbi
Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semi-structured data as graphs where nodes correspo…