Benchmarking Algorithms for Food Localization and Semantic Segmentation
We create a new dataset composed of 120,000 images of 50 diverse food categories. The images are accurately annotated with pixel-wise annotations. The dataset is augmented with the same 5,000 images but rendered under different acquisition distortions that comprise illuminant change, JPEG compression, Gaussian noise, and Gaussian blur.
Complexity Perception in Images
Visual complexity perception plays an important role in the fields of both psychology and computer vision. We investigate image complexity of different kind of stimuli. We perform different types of psycho-physical experiments and correlate subjective data with objective measures.
Content-based retrieval of remote sensing images
We present an extensive evaluation of visual descriptors for the content-based retrieval of remote sensing (RS) images. The evaluation includes global, local, and Convolutional Neural Network (CNNs) features coupled with four different Content-Based Image Retrieval schemes.
Deep Learning for Product Detection
In this project we use deep learning with the aim of automatically finding products in grocery store shelves.
Food 524 Database
Food524DB is the largest publicly available food dataset with 524 food classes and 247,636 images by merging food classes from existing datasets in the state of the art. This database can be used for food recognition. The database is composed of 247,636 images belonging to 524 food categories.
Automatic food recognition is an important task to support the users in their daily dietary monitoring and to keep tracks of their food consumption. We have designed datasets and algorithms for automatic dietary monitoring of canteen customers based on robust computer vision techniques.
Food-475 database is one of the largest publicly available food database with 475 food classes and 247,636 images obtained by merging four publicly available food databases.
In this paper we investigate the use of a deep Convolutional Neural Network (CNN) to predict …
Image orientation detection using LBP-based features and logistic regression
In this work we present an algorithm for the automatic detection of the image orientation that relies on the image content as described by Local Binary Patterns (LBP).
Image quality assessment (IQA) is a multidimensional research problem and an active and evolving research …
MKL for remote sensing image classification
We propose a strategy for land use classification which exploits Multiple Kernel Learning (MKL) to automatically determine a suitable combination of a set of features without requiring any heuristic knowledge about the classification task.
Multitask Painting Categorization by Deep Multibranch Neural Network
In this project we propose a new deep multibranch neural network to solve the tasks …
User Preferences Modeling and Learning for Pleasing Photo Collage Generation
We consider the problem of how to automatically create pleasing photo collages: given a set of photos and a canvas area, we want to arrange the photos on the canvas in a pleasant unsupervised manner.