Are Sparse Representations Really Relevant for Image Classification?
Graph Chatbot
Chat with Graph Search
Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
Humans have the ability to learn. Having seen an object we can recognise it later. We can do this because our nervous system uses an efficient and robust visual processing and capabilities to learn from sensory input. On the other hand, designing algorithm ...
This paper presents a cursive character recognizer, a crucial module in any Cursive Script Recognition system based on a segmentation and recognition approach. The character classification is achieved by combining the use of Neural Gas (NG) and Learning Ve ...
This paper proposes an algorithm for distributed classification, based on a SVM scheme. The contribution of each support vector is approximated by low complexity distributed thresholding over sub-dictionaries, whose union forms a redundant dictionary of at ...
A sparse classifier is guaranteed to generalize better than a denser one, given they perform identical on the training set. However, methods like Support Vector Machine, even if they produce relatively sparse models, are known to scale linearly as the numb ...
Using a low-level representation of images, like matching pursuit, we introduce a new way of describing objects through a general description using a translation, rotation, and isotropic scale invariant dictionary of basis functions. We then use this descr ...
Statistical pattern recognition occupies a central place in the general context of machine learning techniques, as it provides the theoretical insights and the practical means for solving a variety of problems ranging from character recognition to face rec ...
This paper presents a cursive character recognizer, a crucial module in any Cursive Script Recognition system based on a segmentation and recognition approach. The character classification is achieved by combining the use of Neural Gas (NG) and Learning Ve ...
The goal is to improve recognition rate by optimisation of Mel Frequency Cepstral Coefficients (MFCCs): modifications concern the time-frequency representation used to estimate these coefficients. There are many ways to obtain a spectrum out of a signal wh ...
Vision--based place recognition is a desirable feature for an autonomous mobile system. In order to work in realistic scenarios, a visual recognition algorithm should have two key properties: robustness and adaptability. This paper focuses on the latter, ...
In this paper, we propose an analytical low-level representation of images, obtained by a decomposition process, namely the matching pursuit (MP) algorithm, as a new way of describing objects through a general continuous description using an affine invaria ...