Sentence examples for aspects models from inspiring English sources

Patch co-occurrence is summarized into aspects models, whose relevance is estimated for any new image, and used to evaluate class-dependent patch likelihoods.

The first one, traditionally encountered in aspects models, is that the occurrence of a patch is independent of the image it belongs to, given an aspect.

It covers three important aspects: models for the calculation of the particle particle and particle fluid interaction forces, coupling of discrete element method with computational fluid dynamics to describe particle fluid flow, and the theories for linking discrete to continuum modelling.

The next we discuss the related work regarding temporal aspects modeling for sensor data.

Our work is mainly related to three aspects: modeling in ICN, ICN-based VANETs, and content transmission in VANETs.

A novel approach to scene classification is described by Monay et al. in the paper entitled "Contextual Classification of Image Patches with Latent Aspect Models" which combines patch-based contextual classification with latent aspect models.

The segmentation algorithm and the aspect models are linked through a new variable that distinguishes foreground from background patches.

Interestingly, the aspect models do not need 100% of the 600 labeled images for a good classification performance.

In what follows, we propose two aspect models that estimate patch class-likelihoods based on the decomposition of scenes in a mixture of aspects.

In the final stage of preparing this manuscript, new models were put forward to segment images by combining latent aspect models with quantized local patches.

Based on this idea, we present two aspect models that extend PLSA model [22] for image patch classification in Section 6.