Towards better exploiting convolutional neural networks for remote sensing scene classification

Alzheimer's disease (AD) is associated with neurodegeneration in vulnerable limbic and heteromodal regions of the cerebral cortex, detectable in vivo using magnetic resonance imaging. It is not clear whether abnormalities of cortical anatomy in AD can be reliably measured across different subject samples, how closely they track symptoms, and whether they are detectable prior to symptoms. An exploratory map of cortical thinning in mild AD was used to define regions of interest that were applied in a hypothesis-driven fashion to other subject samples. Results demonstrate a reliably quantifiable in vivo signature of abnormal cortical anatomy in AD, which parallels known regional vulnerability to AD neuropathology. Thinning in vulnerable cortical regions relates to symptom severity even in the earliest stages of clinical symptoms. Furthermore, subtle thinning is present in asymptomatic older controls with brain amyloid binding as detected with amyloid imaging. The reliability and clinical validity of AD-related cortical thinning suggests potential utility as an imaging biomarker. This “disease signature” approach to cortical morphometry, in which disease effects are mapped across the cortical mantle and then used to define ROIs for hypothesis-driven analyses, may provide a powerful methodological framework for studies of neuropsychiatric diseases.

The Cortical Signature of Alzheimer's Disease: Regionally Specific Cortical Thinning Relates to Symptom Severity in Very Mild to Mild AD Dementia and is Detectable in Asymptomatic Amyloid-Positive Individuals

In this paper, we evaluate the generalization power of deep features (ConvNets) in two new scenarios: aerial and remote sensing image classification. We evaluate experimentally ConvNets trained for recognizing everyday objects for the classification of aerial and remote sensing images. ConvNets obtained the best results for aerial images, while for remote sensing, they performed well but were outperformed by low-level color descriptors, such as BIC. We also present a correlation analysis, showing the potential for combining/fusing different ConvNets with other descriptors or even for combining multiple ConvNets. A preliminary set of experiments fusing ConvNets obtains state-of-the-art results for the well-known UCMerced dataset.

/pdf/do-deep-features-generalize-from-everyday-objects-to-remote-1urgf3djk4.pdf

Do deep features generalize from everyday objects to remote sensing and aerial scenes domains

One fundamental problem in object retrieval with the bag-of-visual words (BoW) model is its lack of spatial information. Although various approaches are proposed to incorporate spatial constraints into the BoW model, most of them are either too strict or too loose so that they are only effective in limited cases. We propose a new spatially-constrained similarity measure (SCSM) to handle object rotation, scaling, view point change and appearance deformation. The similarity measure can be efficiently calculated by a voting-based method using inverted files. Object retrieval and localization are then simultaneously achieved without post-processing. Furthermore, we introduce a novel and robust re-ranking method with the k-nearest neighbors of the query for automatically refining the initial search results. Extensive performance evaluations on six public datasets show that SCSM significantly outperforms other spatial models, while k-NN re-ranking outperforms most state-of-the-art approaches using query expansion.

/pdf/object-retrieval-and-localization-with-spatially-constrained-1gctpdvovr.pdf

Object retrieval and localization with spatially-constrained similarity measure and k-NN re-ranking

Semantic content-based image retrieval: A comprehensive study ☆

Image re-ranking and rank aggregation based on similarity of ranked lists

The objective of Content-based Image Retrieval (CBIR) systems is to return a ranked list containing the most similar images in a collection given a query image. The effectiveness of these systems is very dependent on the accuracy of the distance function adopted. In this paper, we present a novel approach for redefining distances and later reranking images aiming to improve the effectiveness of CBIR systems. In our approach, distance among images are redefined based on the similarity of their ranked lists. Conducted experiments involving shape, color, and texture descriptors demonstrate the effectiveness of our method.

A scalable re-ranking method for content-based image retrieval

Unsupervised manifold learning through reciprocal kNN graph and Connected Components for image retrieval tasks

This paper presents a shape descriptor based on a set of features computed for each point of an object contour. We also present an algorithm for distance optimization based on the similarity among ranked lists. Experiments were conducted on two well-known data sets: MPEG-7 and Kimia. Experimental results demonstrate that the combination of the two methods is very effective and yields better results than recently proposed shape

Daniel Carlos Guimarães Pedronette

Papers

Image re-ranking and rank aggregation based on similarity of ranked lists

Image re-ranking and rank aggregation based on similarity of ranked lists

A scalable re-ranking method for content-based image retrieval

Unsupervised manifold learning through reciprocal kNN graph and Connected Components for image retrieval tasks

Shape Retrieval using Contour Features and Distance Optimization.