58,405 research outputs found
A Review of Codebook Models in Patch-Based Visual Object Recognition
The codebook model-based approach, while ignoring any structural aspect in vision, nonetheless provides state-of-the-art performances on current datasets. The key role of a visual codebook is to provide a way to map the low-level features into a fixed-length vector in histogram space to which standard classifiers can be directly applied. The discriminative power of such a visual codebook determines the quality of the codebook model, whereas the size of the codebook controls the complexity of the model. Thus, the construction of a codebook is an important step which is usually done by cluster analysis. However, clustering is a process that retains regions of high density in a distribution and it follows that the resulting codebook need not have discriminant properties. This is also recognised as a computational bottleneck of such systems. In our recent work, we proposed a resource-allocating codebook, to constructing a discriminant codebook in a one-pass design procedure that slightly outperforms more traditional approaches at drastically reduced computing times. In this review we survey several approaches that have been proposed over the last decade with their use of feature detectors, descriptors, codebook construction schemes, choice of classifiers in recognising objects, and datasets that were used in evaluating the proposed methods
An Evaluation of Popular Copy-Move Forgery Detection Approaches
A copy-move forgery is created by copying and pasting content within the same
image, and potentially post-processing it. In recent years, the detection of
copy-move forgeries has become one of the most actively researched topics in
blind image forensics. A considerable number of different algorithms have been
proposed focusing on different types of postprocessed copies. In this paper, we
aim to answer which copy-move forgery detection algorithms and processing steps
(e.g., matching, filtering, outlier detection, affine transformation
estimation) perform best in various postprocessing scenarios. The focus of our
analysis is to evaluate the performance of previously proposed feature sets. We
achieve this by casting existing algorithms in a common pipeline. In this
paper, we examined the 15 most prominent feature sets. We analyzed the
detection performance on a per-image basis and on a per-pixel basis. We created
a challenging real-world copy-move dataset, and a software framework for
systematic image manipulation. Experiments show, that the keypoint-based
features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and
Zernike features perform very well. These feature sets exhibit the best
robustness against various noise sources and downsampling, while reliably
identifying the copied regions.Comment: Main paper: 14 pages, supplemental material: 12 pages, main paper
appeared in IEEE Transaction on Information Forensics and Securit
Learning Deep NBNN Representations for Robust Place Categorization
This paper presents an approach for semantic place categorization using data
obtained from RGB cameras. Previous studies on visual place recognition and
classification have shown that, by considering features derived from
pre-trained Convolutional Neural Networks (CNNs) in combination with part-based
classification models, high recognition accuracy can be achieved, even in
presence of occlusions and severe viewpoint changes. Inspired by these works,
we propose to exploit local deep representations, representing images as set of
regions applying a Na\"{i}ve Bayes Nearest Neighbor (NBNN) model for image
classification. As opposed to previous methods where CNNs are merely used as
feature extractors, our approach seamlessly integrates the NBNN model into a
fully-convolutional neural network. Experimental results show that the proposed
algorithm outperforms previous methods based on pre-trained CNN models and
that, when employed in challenging robot place recognition tasks, it is robust
to occlusions, environmental and sensor changes
Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval
This paper presents a new state-of-the-art for document image classification
and retrieval, using features learned by deep convolutional neural networks
(CNNs). In object and scene analysis, deep neural nets are capable of learning
a hierarchical chain of abstraction from pixel inputs to concise and
descriptive representations. The current work explores this capacity in the
realm of document analysis, and confirms that this representation strategy is
superior to a variety of popular hand-crafted alternatives. Experiments also
show that (i) features extracted from CNNs are robust to compression, (ii) CNNs
trained on non-document images transfer well to document analysis tasks, and
(iii) enforcing region-specific feature-learning is unnecessary given
sufficient training data. This work also makes available a new labelled subset
of the IIT-CDIP collection, containing 400,000 document images across 16
categories, useful for training new CNNs for document analysis
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