Local features are not lonely – Laplacian sparse coding for image classification

AEA-Net:Affinity-supervised entanglement attentive network for person re-identification

Non-negativity and locality constrained Laplacian sparse coding for image classification

Recent research has shown the initial success of sparse coding (Sc) in solving many computer vision tasks. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which helps in finding a sparse representation of nonlinear features, we propose kernel sparse representation (KSR). Essentially, KSR is a sparse coding technique in a high dimensional feature space mapped by an implicit mapping function. We apply KSR to feature coding in image classification, face recognition, and kernel matrix approximation. More specifically, by incorporating KSR into spatial pyramid matching (SPM), we develop KSRSPM, which achieves a good performance for image classification. Moreover, KSR-based feature coding can be shown as a generalization of efficient match kernel and an extension of Sc-based SPM. We further show that our proposed KSR using a histogram intersection kernel (HIK) can be considered a soft assignment extension of HIK-based feature quantization in the feature coding process. Besides feature coding, comparing with sparse coding, KSR can learn more discriminative sparse codes and achieve higher accuracy for face recognition. Moreover, KSR can also be applied to kernel matrix approximation in large scale learning tasks, and it demonstrates its robustness to kernel matrix approximation, especially when a small fraction of the data is used. Extensive experimental results demonstrate promising results of KSR in image classification, face recognition, and kernel matrix approximation. All these applications prove the effectiveness of KSR in computer vision and machine learning tasks.

A CNN model embedded with local feature knowledge and its application to time-varying signal classification

Decision letter: Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila

Multi-label tongue image classification via label semantics-driven feature interaction enhancement.

Local binary pattern (LBP) is an important image texture feature extraction method. This paper proposes a combination of adaptive threshold and directional local image feature representation, and to realize the image retrieval. This local combination feature is composed of two parts: 1)The standard deviation of the gray value of all pixels in the local neighborhood of the image and the gray value of the center pixel are used as the comparison threshold for encoding. This threshold is adaptively changed following the grayscale changes in the neighborhood, and can indicate the severity of the gray scale change; 2)directional local pattern: by comparing the grayscale changes of local neighborhood pixels in different directions, discrimination and binary encoding are performed to indicate the local texture features directionality. Finally, the two features are combined to form the local features of the image, and the features are used for image retrieval. Experiments on the Corel-1k database show that this method has a better image retrieval effect than existing local feature representation methods.

This paper deals with the retrieval of scene-of-cri me (or scene) shoeprint images from a reference database of shoeprint images by using a n ew local feature detector and an improved local feature descriptor. Our approach is based on novel modifications and improvements of a few recent techniques in this area: (1) the scale a dapted Harris detector, which is an extension to multi-scale domains of the Harris corner detector; (2) automatic scale selection by the characteristic scale of a local structure. (3) SIFT (Scale-Invariant Feature Transform), one of the most widely investigated descriptors in recent year s. Like most of other local feature representations, the proposed approach can also be divided into two stages: (i) a set of distinctive local features are selected by first de tecting scale adaptive Harris corners where each of them is associated with a scale factor, and then selecting as the final result only those corners whose scale matches the scale of blob-like structur es around them. Here, the scale of a blob-like structure is detected by the Laplace-based scale se lection. (ii). for each feature detected, an enhanced SIFT descriptor is computed to represent t his feature. Our improvements lead two novel methods which we call the Modified Harris-Laplace (MHL) detector, and the enhanced SIFT descriptor. In this paper, we demonstrate the application of t he proposed scheme to the shoeprint image retrieval problem using six sets of synthetic scene images, 50 images for each, and a database of 500 reference shoeprint images. The ret rieval performance of the proposed approach is significantly better, in terms of cumul ative matching score, than the existing methods used in this application area, such as edge directional histogram, power spectral distribution, and pattern & topological spectra.

Accurate and interpretable AI models play a critical role in medical image analysis. However, despite advancements in explainable AI (XAI), existing methods struggle with inconsistent interpretability. To overcome this limitation, we introduce the Integrated Latent and Attention Mapping (ILAM) framework, which enhances both classification accuracy and explainability by fusing local and global feature representations. ILAM integrates a custom-designed Autoencoder (AE) with a Vision Transformer (ViT), where the AE learns fine-grained local features through unsupervised patchwise image reconstruction in the latent space. These local features are then fused with global representations extracted by ViT, creating a hybrid model that improves both performance and post hoc interpretability. To refine explainability, ILAM incorporates a modified attention rollout mechanism, which recursively aggregates latent feature representations and attention weights to produce precise and stable activation maps. We evaluate ILAM on three publicly available medical imaging datasets-BreakHis, Chest X-Ray, and Retinal, demonstrating its superior performance over transformer-based models such as ViT, DeiT, CvT, and SwinT. ILAM consistently generates detailed and reliable activation maps, providing clearer visualizations of critical image regions influencing model decisions. By effectively combining local and global feature fusion, ILAM establishes itself as a robust and interpretable framework for medical image classification.

Image representation based on sparse coding generalizes the bag of words model. Although it reduces the reconstruction error for local features to achieve the state-of-the-art image classification performance, the large computational cost hinders the application of sparse coding-based image features. In this paper, we propose approximating a sparse code using the output of a simple neural network. The resulting parameter learning model for the neural network automatically incorporates non-negative and shift-invariant constraints, leading to an efficient normalized non-negative sparse coding (N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> SC) sparse encoder. Without the use of the traditional iterative process to solve the sparse coding objective, the sparse encoder directly “converts” each local feature into a sparse code. We also introduce a method for training the encoder based on the auto-encoder method. In addition, we formally propose the corresponding sparse coding scheme called N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> SC, which enforces both the non-negative constraint and the shift-invariant constraint in addition to the traditional sparse coding criteria. As demonstrated by several experiments, the obtained N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> SC encoder requires only 3%-10% of the processing time for image feature extraction compared with the standard sparse coding scheme. At the same time, the features extracted using the exact solutions of the N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> SC coding scheme and the N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> SC encoder offer superior image classification accuracy compared to the accuracy of many existing sparse coding-based representations.

Fast human action classification and VOI localization with enhanced sparse coding

Non-negativity and dependence constrained sparse coding for image classification

In this study, we investigated how shapes are classified based on local and global features by four representative convolutional neural networks (CNNs), i.e., AlexNet, VGG, ResNet and Inception. While the local features are based on simple components, such as orientation of line segment, the global features are based on the whole object, such as whether an object has a hole. For example, solid triangles and solid squares are differentiated by local features, solid circles and rings are differentiated by a global feature. Two sets of experiments were performed in this research. In the first experiment, we examined how the four CNNs pre-trained on ImageNet (with transfer learning) learned to differentiate the regular shapes (equilateral-triangles, squares, circles and rings). Our results showed that the pre-trained CNNs exhibited faster learning rates in the tasks discriminating the local features than in the tasks discriminating the global feature. However, the transfer learning of discriminating the global feature in regular shapes were better generalized to irregular shapes than the transfer learning of discriminating local features. In the second experiment, the CNNs were trained from scratch (with random weights initialization) to discriminate local and global features in regular and irregular shapes. Different from the transfer learning, the CNNs exhibited faster learning rates in discriminating the global feature than the local features. Similar to transfer learning, the CNNs exhibited excellent learning generalization to discriminating the global feature of irregular shapes, but poor learning generalization to discriminating the local features in the irregular shapes. The overarching goal of this research is to create a paradigm and benchmark to directly compare how the CNNs and primate visual systems process geometrical invariants. In contrast to the ImageNet approach which employs natural images to train CNNs, we employed the “ShapeNet” approach which features geometrical shapes with well-defined properties. The ShapeNet approach will not only help elucidate the strengths and limitations of CNN computation, but also provide insights into visual information processing in the primates.

Image representation using local features and sparse coding (SC) plays a very important role in image classification when the dataset is fairly large. Despite of its worldwide popularity, there are still some improving space in classification efficiency and computational investment in training and coding phrase of SC. In this paper, we put forward a novel object categories recognition method from two aspects. First, the contextual relevance between image patches are fully utilized by merging local feature of every sub-patch with its neighboring ones into strong context features to generate the multiple sparse representations, which are received by the SC and multi-scale max pooling SPM(Spatial Pyramid Matching), respectively. Second, while calculating the sparse coefficients of SC, we need to solve L1-regularized least square problem. Screening out the zero coefficients and discarding the corresponding inactive codewords before solving Lasso problem can remarkably speed up the optimization. The proposed method outperforms state-of-the-art performance in a large number of image categorization experiments on several benchmarks: the ground truth dataset (21 Land-Use database), the event dataset (UIUC-Sport dataset), and the object recognition dataset (Caltech101 dataset).

Image representation using local features and sparse coding (SC) plays a very important role in image classification when the dataset is fairly large. Despite of its worldwide popularity, there are still some improving space in classification efficiency and computational investment in training and coding phrase of SC. In this paper, we put forward a novel object categories recognition method from two aspects. First, the contextual relevance between image patches are fully utilized by merging local feature of every sub-patch with its neighboring ones into strong context features to generate the multiple sparse representations, which are received by the SC and multi-scale max pooling SPM(Spatial Pyramid Matching), respectively. Second, while calculating the sparse coefficients of SC, we need to solve L1-regularized least square problem. Screening out the zero coefficients and discarding the corresponding inactive codewords before solving Lasso problem can remarkably speed up the optimization. The proposed method outperforms state-of-the-art performancein a large number of image categorization experiments on several benchmarks: the ground truth dataset (21 Land-Use database), the event dataset (UIUC-Sport dataset), and the object recognition dataset (Caltech101 dataset).