26,411 research outputs found
Why my photos look sideways or upside down? Detecting Canonical Orientation of Images using Convolutional Neural Networks
Image orientation detection requires high-level scene understanding. Humans
use object recognition and contextual scene information to correctly orient
images. In literature, the problem of image orientation detection is mostly
confronted by using low-level vision features, while some approaches
incorporate few easily detectable semantic cues to gain minor improvements. The
vast amount of semantic content in images makes orientation detection
challenging, and therefore there is a large semantic gap between existing
methods and human behavior. Also, existing methods in literature report highly
discrepant detection rates, which is mainly due to large differences in
datasets and limited variety of test images used for evaluation. In this work,
for the first time, we leverage the power of deep learning and adapt
pre-trained convolutional neural networks using largest training dataset
to-date for the image orientation detection task. An extensive evaluation of
our model on different public datasets shows that it remarkably generalizes to
correctly orient a large set of unconstrained images; it also significantly
outperforms the state-of-the-art and achieves accuracy very close to that of
humans
Why my photos look sideways or upside down? Detecting Canonical Orientation of Images using Convolutional Neural Networks
Image orientation detection requires high-level scene understanding. Humans
use object recognition and contextual scene information to correctly orient
images. In literature, the problem of image orientation detection is mostly
confronted by using low-level vision features, while some approaches
incorporate few easily detectable semantic cues to gain minor improvements. The
vast amount of semantic content in images makes orientation detection
challenging, and therefore there is a large semantic gap between existing
methods and human behavior. Also, existing methods in literature report highly
discrepant detection rates, which is mainly due to large differences in
datasets and limited variety of test images used for evaluation. In this work,
for the first time, we leverage the power of deep learning and adapt
pre-trained convolutional neural networks using largest training dataset
to-date for the image orientation detection task. An extensive evaluation of
our model on different public datasets shows that it remarkably generalizes to
correctly orient a large set of unconstrained images; it also significantly
outperforms the state-of-the-art and achieves accuracy very close to that of
humans
Context-aware person identification in personal photo collections
Identifying the people in photos is an important need for users of photo management systems. We present MediAssist, one such system which facilitates browsing, searching and semi-automatic annotation of personal photos, using analysis of both image content and the context in which the photo is captured. This semi-automatic annotation includes annotation of the identity of people in photos. In this paper, we focus on such person annotation, and propose person identiďŹcation techniques based on a combination of context and content. We propose language modelling and nearest neighbor approaches to context-based person identiďŹcation, in addition to novel face color and image color content-based features (used alongside face recognition and body patch features). We conduct a comprehensive empirical study of these techniques using the real private photo collections of a number of users, and show that combining context- and content-based analysis improves performance over content or context alone
Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis
The availability of large-scale annotated image datasets and recent advances
in supervised deep learning methods enable the end-to-end derivation of
representative image features that can impact a variety of image analysis
problems. Such supervised approaches, however, are difficult to implement in
the medical domain where large volumes of labelled data are difficult to obtain
due to the complexity of manual annotation and inter- and intra-observer
variability in label assignment. We propose a new convolutional sparse kernel
network (CSKN), which is a hierarchical unsupervised feature learning framework
that addresses the challenge of learning representative visual features in
medical image analysis domains where there is a lack of annotated training
data. Our framework has three contributions: (i) We extend kernel learning to
identify and represent invariant features across image sub-patches in an
unsupervised manner. (ii) We initialise our kernel learning with a layer-wise
pre-training scheme that leverages the sparsity inherent in medical images to
extract initial discriminative features. (iii) We adapt a multi-scale spatial
pyramid pooling (SPP) framework to capture subtle geometric differences between
learned visual features. We evaluated our framework in medical image retrieval
and classification on three public datasets. Our results show that our CSKN had
better accuracy when compared to other conventional unsupervised methods and
comparable accuracy to methods that used state-of-the-art supervised
convolutional neural networks (CNNs). Our findings indicate that our
unsupervised CSKN provides an opportunity to leverage unannotated big data in
medical imaging repositories.Comment: Accepted by Medical Image Analysis (with a new title 'Convolutional
Sparse Kernel Network for Unsupervised Medical Image Analysis'). The
manuscript is available from following link
(https://doi.org/10.1016/j.media.2019.06.005
Multi modal multi-semantic image retrieval
PhDThe rapid growth in the volume of visual information, e.g. image, and video can
overwhelm usersâ ability to find and access the specific visual information of interest
to them. In recent years, ontology knowledge-based (KB) image information retrieval
techniques have been adopted into in order to attempt to extract knowledge from these
images, enhancing the retrieval performance. A KB framework is presented to
promote semi-automatic annotation and semantic image retrieval using multimodal
cues (visual features and text captions). In addition, a hierarchical structure for the KB
allows metadata to be shared that supports multi-semantics (polysemy) for concepts.
The framework builds up an effective knowledge base pertaining to a domain specific
image collection, e.g. sports, and is able to disambiguate and assign high level
semantics to âunannotatedâ images.
Local feature analysis of visual content, namely using Scale Invariant Feature
Transform (SIFT) descriptors, have been deployed in the âBag of Visual Wordsâ
model (BVW) as an effective method to represent visual content information and to
enhance its classification and retrieval. Local features are more useful than global
features, e.g. colour, shape or texture, as they are invariant to image scale, orientation
and camera angle. An innovative approach is proposed for the representation,
annotation and retrieval of visual content using a hybrid technique based upon the use
of an unstructured visual word and upon a (structured) hierarchical ontology KB
model. The structural model facilitates the disambiguation of unstructured visual
words and a more effective classification of visual content, compared to a vector
space model, through exploiting local conceptual structures and their relationships.
The key contributions of this framework in using local features for image
representation include: first, a method to generate visual words using the semantic
local adaptive clustering (SLAC) algorithm which takes term weight and spatial
locations of keypoints into account. Consequently, the semantic information is
preserved. Second a technique is used to detect the domain specific ânon-informative
visual wordsâ which are ineffective at representing the content of visual data and
degrade its categorisation ability. Third, a method to combine an ontology model with
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a visual word model to resolve synonym (visual heterogeneity) and polysemy
problems, is proposed. The experimental results show that this approach can discover
semantically meaningful visual content descriptions and recognise specific events,
e.g., sports events, depicted in images efficiently.
Since discovering the semantics of an image is an extremely challenging problem, one
promising approach to enhance visual content interpretation is to use any associated
textual information that accompanies an image, as a cue to predict the meaning of an
image, by transforming this textual information into a structured annotation for an
image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct
types of information representation and modality, there are some strong, invariant,
implicit, connections between images and any accompanying text information.
Semantic analysis of image captions can be used by image retrieval systems to
retrieve selected images more precisely. To do this, a Natural Language Processing
(NLP) is exploited firstly in order to extract concepts from image captions. Next, an
ontology-based knowledge model is deployed in order to resolve natural language
ambiguities. To deal with the accompanying text information, two methods to extract
knowledge from textual information have been proposed. First, metadata can be
extracted automatically from text captions and restructured with respect to a semantic
model. Second, the use of LSI in relation to a domain-specific ontology-based
knowledge model enables the combined framework to tolerate ambiguities and
variations (incompleteness) of metadata. The use of the ontology-based knowledge
model allows the system to find indirectly relevant concepts in image captions and
thus leverage these to represent the semantics of images at a higher level.
Experimental results show that the proposed framework significantly enhances image
retrieval and leads to narrowing of the semantic gap between lower level machinederived
and higher level human-understandable conceptualisation
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
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