Building kernels from binary strings for image matching

Kernel methods in …, 2004

In this paper we present a new algorithm suitable for matching discrete objects such as strings and trees in linear time, thus obviating dynarrtic programming with quadratic time complexity. Furthermore, prediction cost in many cases can be reduced to linear cost in the length of the sequence to be classified, regardless of the number of support vectors. This improvement on the currently available algorithms makes string kernels a viable alternative for the practitioner.

2005

We introduce the Intermediate Matching (IM) kernel for SVM-based object recognition. The IM kernel operates on a feature space of vector sets where each image is represented by a set of local features. Matching algorithms have proved to be efficient for such types of features. Nevertheless, kernelizing the matching for SVM does not lead to positive definite kernels. The IM kernel overcomes this drawback, as it mimics matching algorithms while being positive definite. The IM kernel introduces an intermediary set of so-called virtual local features. These select the pairs of local features to be matched. Comparisons with the Matching kernel shows that the IM kernels leads to similar performances.

2005

Abstract Colour is the most widely used attribute in image retrieval and object recognition. A technique known as histogram intersection has been widely studied and is considered to be effective for color-image indexing. The key issue of this algorithm is the selection of an appropriate color space and optimal quantization of the selected color space.

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017

We present a new kernel on fuzzy sets: the cross product kernel on fuzzy sets which can be used to estimate similarity measures between fuzzy sets with a geometrical interpretation in terms of inner products. We show that this kernel is a particular case of the convolution kernel and it generalizes the widely-know kernel on sets towards the space of fuzzy sets. Moreover, we show that the cross product kernel on fuzzy sets performs an embedding of probability measures into a reproduction kernel Hilbert space. Finally, we experimentally show the applicability of this kernel on a supervised classification task on noisy datasets.

PLoS ONE, 2014

This paper mainly focuses on how to effectively and efficiently measure visual similarity for local feature based representation. Among existing methods, metrics based on Bag of Visual Word (BoV) techniques are efficient and conceptually simple, at the expense of effectiveness. By contrast, kernel based metrics are more effective, but at the cost of greater computational complexity and increased storage requirements. We show that a unified visual matching framework can be developed to encompass both BoV and kernel based metrics, in which local kernel plays an important role between feature pairs or between features and their reconstruction. Generally, local kernels are defined using Euclidean distance or its derivatives, based either explicitly or implicitly on an assumption of Gaussian noise. However, local features such as SIFT and HoG often follow a heavy-tailed distribution which tends to undermine the motivation behind Euclidean metrics. Motivated by recent advances in feature coding techniques, a novel efficient local coding based matching kernel (LCMK) method is proposed. This exploits the manifold structures in Hilbert space derived from local kernels. The proposed method combines advantages of both BoV and kernel based metrics, and achieves a linear computational complexity. This enables efficient and scalable visual matching to be performed on large scale image sets. To evaluate the effectiveness of the proposed LCMK method, we conduct extensive experiments with widely used benchmark datasets, including 15-Scenes, Caltech101/256, PASCAL VOC 2007 and 2011 datasets. Experimental results confirm the effectiveness of the relatively efficient LCMK method.

Lecture Notes in Computer Science, 2004

During recent years much effort has been spent in incorporating problem specific a-priori knowledge into kernel methods for machine learning. A common example is a-priori knowledge given by a distance measure between objects. A simple but effective approach for kernel construction consists of substituting the Euclidean distance in ordinary kernel functions by the problem specific distance measure. We formalize this distance substitution procedure and investigate theoretical and empirical effects. In particular we state criteria for definiteness of the resulting kernels. We demonstrate the wide applicability by solving several classification tasks with SVMs. Regularization of the kernel matrices can additionally increase the recognition accuracy.

2011

Although “Bag-of-Features” image models have shown very good potential for object matching and image retrieval, such a complex data representation requires computationally expensive similarity measure evaluation. In this paper, we propose a framework unifying dictionary-based and kernel-based similarity functions that highlights the tradeoff between powerful data representation and eff cient similarity computation. On the basis of this formalism, we propose a new kernel-based similarity approach for Bag-of-Feature descriptions. We introduce a method for fast similarity search in large image databases. The conducted experiments prove that our approach is very competitive among State-of-the-art methods for similarity retrieval tasks.

The Journal of Machine Learning Research, 2006

Kernels are two-placed functions that can be interpreted as inner products in some Hilbert space. It is this property which makes kernels predestinated to carry linear models of learning, optimization or classification strategies over to non-linear variants. Following this idea, various kernel-based methods like support vector machines or kernel principal component analysis have been conceived which prove to be successful for machine learning, data mining and computer vision applications. When applying a kernel-based method a central question is the choice and the design of the kernel function. This paper provides a novel view on kernels based on fuzzy-logical concepts which allows to incorporate prior knowledge in the design process. It is demonstrated that kernels mapping to the unit interval with constant one in its diagonal can be represented by a commonly used fuzzylogical formula for representing fuzzy rule bases. This means that a great class of kernels can be represented by fuzzy-logical concepts. Apart from this result, which only guarantees the existence of such a representation, constructive examples are presented and the relation to unlabeled learning is pointed out.

Lecture Notes in Computer Science, 2006

This paper extends the idea of weighted distance functions to kernels and support vector machines. Here, we focus on applications that rely on sliding a window over a sequence of string data. For this type of problems it is argued that a symbolic, context-based representation of the data should be preferred over a continuous, real format as this is a much more intuitive setting for working with (weighted) distance functions. It is shown how a weighted string distance can be decomposed and subsequently used in di«erent kernel functions and how these kernel functions correspond to real kernels between the continuous, real representations of the symbolic, context-based representations of the vectors. Author funded by a doctoral grant of the institute for advancement of scientific technological research in Flanders (IWT). Author funded by a doctoral grant of the Vrije Universiteit Brussel.

International Journal of Data Mining, Modelling and Management, 2011

The subject of this paper is the kernel method, which offers an elegant solution to improve the effectiveness and the efficiency of the learning process in an Image Retrieval System. By simulating learning in high-dimensional feature spaces while working with the original low-dimensional input data, kernel methods provide a way for obtaining nonlinear decision boundaries from algorithms previously restricted to handling only linearly separable image collections. The aim of this paper is to summarize results that have been obtained by using the kernel method in Image Retrieval Systems. First, the paper introduces the kernel method together with some of its useful properties, and several kernel types used in fields like Image Retrieval, Text Mining, Machine Learning, and Computational Biology. Then, the paper focuses on applications and research questions involving the kernel method, such as kernel-based learning, kernel selection, and feature selection methods.