This paper parallelizes the spatial pyramid match kernel (SPK) implementation. SPK is one of the most usable kernel methods, along with support vector machine classifier, with high accuracy in object recognition. MATLAB parallel computing toolbox has been used to parallelize SPK. In this implementation, MATLAB Message Passing Interface (MPI) functions and features included in the toolbox help u...

Spatial analysis of salient feature points has been shown to be promising in image analysis and classification. In the past, spatial pyramid matching makes use of both of salient feature points and spatial multiresolution blocks to match between images. However, it is shown that different images or blocks can still have similar features using spatial pyramid matching. The analysis and matching ...

With the success of local features in object recognition, feature-set representations are widely used in computer vision and related domains. Pyramid match kernel (PMK) is an efficient approach to quantifying the similarity between two unordered feature-sets, which allows well established kernel machines to learn with such representations. However, the approximation of PMK to the optimal featur...

There have been many approaches to tackle the problem of image classification. Some sacrifice speed for a very complex model whereas other approaches try to limit the features for speed. In our approach, we try to maintain spatial information similar to the spatial pyramid approach and use the pyramid match kernel for efficiency. We split the images into overlapping patches at multiple scales a...

This paper describes the participation of the CPPP/UFMS group in the robot vision task. We have applied the spatial pyramid matching proposed by Lazebnik et al. This method extends bag-of-visualwords to spatial pyramids by concatenating histograms of local features found in increasingly fine sub-regions. To form the visual vocabulary, kmeans clustering was applied in a random subset of images f...

In numerous domains it is useful to represent a single example by the set of the local features or parts that comprise it. However, this representation poses a challenge to many conventional machine learning techniques, since sets may vary in cardinality and elements lack a meaningful ordering. Kernel methods can learn complex functions, but a kernel over unordered set inputs must somehow solve...

Recent research has shown the effectiveness of using sparse coding(Sc) to solve many computer vision problems. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which may reduce the feature quantization error and boost the sparse coding performance, we propose Kernel Sparse Representation(KSR). KSR is essentially the sparse coding technique in a high dime...

In this paper, we propose an object tracking framework based on a spatial pyramid heat kernel structural information representation. In the tracking framework, we take advantage of heat kernel structural information (HKSI) matrices to represent object appearance, because HKSI matrices perform well in characterizing the edge flow (or structural) information on the object appearance graph. To fur...

Spatial pyramid matching has recently become a promising technique for image classification. Despite its success and popularity, no prior work has tackled the problem of learning the optimal spatial pyramid representation for the given image data and the associated object category. We propose a Multiple Scale Learning (MSL) framework to learn the best weights for each scale in the pyramid. Our ...

Recently, the Support Vector Machine (SVM) using Spatial Pyramid Matching (SPM) kernel has achieved remarkable successful in image classification. The classification accuracy can be improved further when combining the sparse coding with SPM. However, the existing methods give the same weight of patches of SPM at different levels. Clearly the discriminative powers of SPM at different levels are ...