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Histopathology image classification can provide automated support towards cancer diagnosis. In this paper, we present a transfer learning-based approach for histopathology image classification. We first represent the image feature by Fisher Vector (FV) encoding of local features that are extracted using the Convolutional Neural Network (CNN) model pretrained on ImageNet. Next, to better transfer the...
A label consistent recursive least squares dictionary learning algorithm, LC-RLSDLA, is proposed to learn discriminative dictionaries for image classification based on sparse coding. The class label information and a label consistency term are used in the cost function to enforce discriminability among the sparse codes. Two operation modes are derived for the LC-RLSDLA: the supervised learning mode,...
In image classification and retrieval, the semantic gap is the major challenge. It characterizes the difference between human perception of a concept and how it can be represented using machine level language. Bag of visual words is a well-known efficient method for image representation, however it showed some limitations. The loss of information during the vector quantization process is one of these...
Recently, the sparse coding based image representation has achieved state-of-the-art recognition results on many benchmarks. In this paper, we propose Multi-cue Normalized Non-Negative Sparse Encoder (MN3SE) which enforces both the non-negative constraint and the shift-invariant constraint on top of the traditional sparse coding criteria, and takes multi-cue to further boost the performance. The former...
The bag-of-features based models are widely used for image classification. In these models, an image is represented as a set of visual words which come from a dictionary. Therefore, a well learned dictionary is responsible for the discriminative power of representations of images. Our observations show that the representation of an image carries rich underlying information of a dictionary, so we propose...
The linear coding methods for image classification work by projecting each local descriptor into the codebook, and making a tradeoff between minimizing the projection error and representation sparseness or locality. In this procedure, it is inevitable to lose some discriminative information which may be very important for image classification. In this paper, we alleviate the information loss in the...
The bag of visual words (BoW) model is one of the most successful model in image classification task. However, the major problem of the BoW model lies in the determination of visual words, which consists of codebook training and feature encoding phases. The traditional K-means and hard-assignment method completely ignore the structure of the local feature space, leading to high loss of information...
Codebook plays an important role in the bag-of-visual-words (BoW) model for image classification. However, the traditional codebook generation procedure ignores the spatial information. Although a lot of works have been done to consider the spatial information for codebook generation, most of them rely on fixed region selection or partition of images, hence are not able to cope with the variations...
Recently, dictionary learned by sparse coding has been widely adopted in image classification and has achieved competitive performance. Sparse coding is capable of reducing the reconstruction error in transforming low-level descriptors into compact mid-level features. Nevertheless, dictionary learned by sparse coding does not have the ability to distinguish different classes. That is to say, it is...
Bag-of-words (BoW) model is widely used for image classification. Recently, the framework of sparse coding and max pooling proved an effective approach for image classification. Max pooling adopts a winner-take-all strategy. Thus, it can be regarded as a codebook weighting process. The results of this process are the weights of the associated codebook. However, there are high intra-class variations...
Bag-of-features has become very popular in Image classification. Offline codebook learning has to limit the number of training sample concerned with memory, and it influences classification accuracy to some extent. We propose an online sparse learning algorithm, which utilizes the reconstruction error to update the current codebook. It can capture salient properties of images in real-time. Most of...
We consider the feature extraction problem based on compressive sampling for supervised image classification. Inspired by recently emerged 1D compressive sampling (1DCS) and 2DPCA techniques, a novel 2D compressive sampling method, called 2DCS, using two random underdetermined projections, is proposed. 2DCS data could be effectively used for pattern representation. Moreover, original data could be...
In this paper, a novel Sparsely Encoded Local Descriptor (SELD) is proposed for face recognition. Compared with K-means or Random-projection tree based previous methods, sparsity constraint is introduced in our dictionary learning and sequent image encoding, which implies more stable and discriminative face representation. Sparse coding also leads to an image descriptor of summation of sparse coefficient...
Succeeding in determining information about the origin of a digital image is a basic issue of multimedia forensics. In particular it could be interesting to individuate which is the specific camera (brand and/or model) that has taken that photo; to do that, additional knowledge are needed about the camera such as its fingerprint, usually computed by resorting at the extraction of the PRNU (Photo-Response-Uniformity-Noise)...
Local Binary Pattern (LBP) has been widely used in texture classification because of its simplicity and computational efficiency. Traditional LBP codes the sign of the local difference and uses the histogram of the binary code to model the given image. However, the directional statistical information is ignored in LBP. In this paper, some directional statistical features, specifically the mean and...
In this paper we investigate the usage of random ortho-projections in the compression of sparse feature vectors. The study is carried out by evaluating the compressed features in classification tasks instead of concentrating on reconstruction accuracy. In the random ortho-projection method, the mapping for the compression can be obtained without any further knowledge of the original features. This...
We present a meta-learning framework for the design of potential functions for Conditional Random Fields. The design of both node potential and edge potential is formulated as a classification problem where margin classifiers are used. The set of state transitions for the edge potential is treated as a set of different classes, thus defining a multi-class learning problem. The Error-Correcting Output...
Many content-based image mining systems extract local features from images to obtain an image description based on discrete feature occurrences. Such applications require a visual vocabulary also known as visual codebook or visual dictionary to discretize the extracted high-dimensional features to visual words in an efficient yet accurate way. Once such an application operates on images of a very...
This paper reviews the current soft computing (SC) techniques employed in image steganography as well as proposes a new hybrid approach of these SC techniques to exploit their complementary strengths. Four main SC techniques in image steganography - neural network (NN), genetic algorithm (GA), support vector machines (SVM) and fuzzy logic (FL) are assessed based on the three main measurements of steganography...
The traditional SPM approach based on bag-of-features (BoF) requires nonlinear classifiers to achieve good image classification performance. This paper presents a simple but effective coding scheme called Locality-constrained Linear Coding (LLC) in place of the VQ coding in traditional SPM. LLC utilizes the locality constraints to project each descriptor into its local-coordinate system, and the projected...
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