Latent Topics-based Relevance Feedback for Video Retrieval

This paper presents a novel Content-Based Video Retrieval approach in order to cope with the semantic gap challenge by means of latent topics. Firstly, a supervised topic model is proposed to transform the classical retrieval approach into a class discovery problem. Subsequently, a new probabilistic ranking function is deduced from that model to tackle the semantic gap between low-level features and high-level concepts. Finally, a short-term relevance feedback scheme is defined where queries can be initialised with samples from inside or outside the database. Several retrieval simulations have been carried out using three databases and seven different ranking functions to test the performance of the presented approach. Experiments revealed that the proposed ranking function is able to provide a competitive advantage within the content-based retrieval field

Sparse Multi-modal probabilistic Latent Semantic Analysis for Single-Image Super-Resolution

This paper presents a novel single-image super-resolution (SR) approach based on latent topics in order to take advantage of the semantics pervading the topic space when super-resolving images. Image semantics has shown to be useful to relieve the ill-posed nature of the SR problem, however the most accepted clustering-based approach used to define semantic concepts limits the capability of representing complex visual relationships. The proposed approach provides a new probabilistic perspective where the SR process is performed according to the semantics encapsulated by a new topic model, the Sparse Multimodal probabilistic Latent Semantic Analysis (sMpLSA). Firstly, the sMpLSA model is formulated. Subsequently, a new SR framework based on sMpLSA is defined. Finally, an experimental comparison is conducted using seven learningbased SR methods over three different image datasets. Experiments reveal the potential of latent topics in SR by reporting that the proposed approach is able to provide a competitive performance

Incremental probabilistic Latent Semantic Analysis for video retrieval

Recent research trends in Content-based Video Retrieval have shown topic models as an effective tool to deal with the semantic gap challenge. In this scenario, this paper has a dual target: (1) it is aimed at studying how the use of different topic models (pLSA, LDA and FSTM) affects video retrieval performance; (2) a novel incremental topic model (IpLSA) is presented in order to cope with incremental scenarios in an effective and efficient way. A comprehensive comparison among these four topic models using two different retrieval systems and two reference benchmarking video databases is provided. Experiments revealed that pLSA is the best model in sparse conditions, LDA tend to outperform the rest of the models in a dense space and IpLSA is able to work properly in both cases

Integral imaging acquisition and processing for visualization of photon counting images in the mid-wave infrared range

Penència presentada al SPIE Conference Volume 9867 "Three-Dimensional Imaging, Visualization, and Display 2016" organitzat per Bahram Javidi i Jung-Young Son i celebrat a Baltimore, (Maryland, United States) el 17 d' abril de 2016In this paper, we present an overview of our previously published work on the application of the maximum likelihood (ML) reconstruction method to integral images acquired with a mid-wave infrared detector on two different types of scenes: one of them consisting of a road, a group of trees and a vehicle just behind one of the trees (being the car at a distance of more than 200m from the camera), and another one consisting of a view of the Wright Air Force Base airfield, with several hangars and different other types of installations (including warehouses) at distances ranging from 600m to more than 2km. Dark current noise is considered taking into account the particular features this type of sensors have. Results show that this methodology allows to improve visualization in the photon counting domain

Intersensor Remote Sensing Image Registration Using Multispectral Semantic Embeddings

This letter presents a novel intersensor registration framework specially designed to register Sentinel-3 (S3) operational data using the Sentinel-2 (S2) instrument as a reference. The substantially higher resolution of the Multispectral Instrument (MSI), on-board S2, with respect to the Ocean and Land Color Instrument (OLCI), carried by S3, makes the former sensor a suitable spatial reference to finely adjust OLCI products. Nonetheless, the important spectral-spatial differences between both instruments may constrain traditional registration mechanisms to effectively align data of such different nature. In this context, the proposed registration scheme advocates the use of a topic model-based embedding approach to conduct the intersensor registration task within a common multispectral semantic space, where the input imagery is represented according to their corresponding spectral feature patterns instead of the low-level attributes. Thus, the OLCI products can be effectively registered to the MSI reference data by aligning those hidden patterns that fundamentally express the same visual concepts across the sensors. The experiments, conducted over four different S2 and S3 operational data collections, reveal that the proposed approach provides performance advantages over six different intersensor registration counterparts

Endmember Extraction From Hyperspectral Imagery Based on Probabilistic Tensor Moments

This letter presents a novel hyperspectral endmember extraction approach that integrates a tensor-based decomposition scheme with a probabilistic framework in order to take advantage of both technologies when uncovering the signatures of pure spectral constituents in the scene. On the one hand, statistical unmixing models are generally able to provide accurate endmember estimates by means of rather complex optimization algorithms. On the other hand, tensor decomposition techniques are very effective factorization tools which are often constrained by the lack of physical interpretation within the remote sensing field. In this context, this letter develops a new hybrid endmember extraction approach based on the decomposition of the probabilistic tensor moments of the hyperspectral data. Initially, the input image reflectance values are modeled as a collection of multinomial distributions provided by a family of Dirichlet generalized functions. Then, the unmixing process is effectively conducted by the tensor decomposition of the thirdorder probabilistic tensor moments of the multivariate data. Our experiments, conducted over four hyperspectral data sets, reveal that the proposed approach is able to provide efficient and competitive results when compared to different state-of-the-art endmember extraction methods

Sentinel-2 and Sentinel-3 Intersensor Vegetation Estimation via Constrained Topic Modeling

This letter presents a novel intersensor vegetation estimation framework, which aims at combining Sentinel-2 (S2) spatial resolution with Sentinel-3 (S3) spectral characteristics in order to generate fused vegetation maps. On the one hand, the multispectral instrument (MSI), carried by S2, provides high spatial resolution images. On the other hand, the Ocean and Land Color Instrument (OLCI), one of the instruments of S3, captures the Earth's surface at a substantially coarser spatial resolution but using smaller spectral bandwidths, which makes the OLCI data more convenient to highlight specific spectral features and motivates the development of synergetic fusion products. In this scenario, the approach presented here takes advantage of the proposed constrained probabilistic latent semantic analysis (CpLSA) model to produce intersensor vegetation estimations, which aim at synergically exploiting MSI's spatial resolution and OLCI's spectral characteristics. Initially, CpLSA is used to uncover the MSI reflectance patterns, which are able to represent the OLCI-derived vegetation. Then, the original MSI data are projected onto this higher abstraction-level representation space in order to generate a high-resolution version of the vegetation captured in the OLCI domain. Our experimental comparison, conducted using four data sets, three different regression algorithms, and two vegetation indices, reveals that the proposed framework is able to provide a competitive advantage in terms of quantitative and qualitative vegetation estimation results

Single-frame super-resolution in remote sensing: a practical overview

Image acquisition technology is improving very fast from a performance point of view. However, there are physical restrictions that can only be solved using software processing strategies. This is particularly true in the case of super resolution (SR) methodologies. SR techniques have found a fertile application field in airborne and space optical acquisition platforms. Single-frame SR methods may be advantageous for some remote-sensing platforms and acquisition time conditions. The contributions of this article are basically two: (1) to present an overview of single-frame SR methods, making a comparative analysis of their performance in different and challenging remote-sensing scenarios, and (2) to propose a new single-frame SR taxonomy, and a common validation strategy. Finally, we should emphasize that, on the one hand, this is the first time, to the best of our knowledge, that such a review and analysis of single SR methods is made in the framework of remote sensing, and, on the other hand, that the new single-frame SR taxonomy is aimed at shedding some light when classifying some types of single-frame SR methods.This work was supported by the Spanish Ministry of Economy under the project ESP2013 - 48458-C4-3-P, by Generalitat Valenciana through project PROMETEO-II/2014/062, and by Universitat Jaume I through project P11B2014-09

Unsupervised colour image segmentation by low-level perceptual grouping

This paper proposes a new unsupervised approach for colour image segmentation. A hierarchy of image partitions is created on the basis of a function that merges spatially connected regions according to primary perceptual criteria. Likewise, a global function that measures the goodness of each defined partition is used to choose the best low-level perceptual grouping in the hierarchy. Contributions also include a comparative study with five unsupervised colour image segmentation techniques. These techniques have been frequently used as a reference in other comparisons. The results obtained by each method have been systematically evaluated using four well-known unsupervised measures for judging the segmentation quality. Our methodology has globally shown the best performance, obtaining better results in three out of four of these segmentation quality measures. Experiments will also show that our proposal finds low-level perceptual solutions that are highly correlated with the ones provided by human

Filter banks for hyperspectral pixel classification of satellite images

Satellite hyperspectral imaging deals with heterogenous images containing different texture areas. Filter banks are frequently used to characterize textures in the image performing pixel classification. This filters are designed using Different scales and orientations in order to cover all areas in the frequential domain. This work is aimed at studying the influence of the different scales used in the analysis, comparing texture analysis theory with hyperspectral imaging necessities. To pursue this, Gabor filters over complex planes and opponent features are taken into account and also compared in the feature extraction proces