A generative model for multi class object recognition and detection

In this study, a generative type probabilistic model is proposed for object recognition. This model is trained by weakly labelled images and performs classification and detection at the same time. When test on highly challenging data sets, the model performs good for both tasks (classification and detection).


Comparison of generative and discriminative techniques for object detection and classification
Ulusoy, İlkay (2004-01-01)
Many approaches to object recognition are founded on probability theory, and can be broadly characterized as either generative or discriminative according to whether or not the distribution of the image features is modelled. Generative and discriminative methods have very different characteristics, as well as complementary strengths and weaknesses. In this chapter we introduce new generative and discriminative models for object detection and classification based on weakly labelled training data. We use thes...
Object Recognition via Local Patch Labelling
Ulusoy, İlkay (2005-03-01)
In recent years the problem of object recognition has received considerable attention from both the machine learning and computer vision communities. The key challenge of this problem is to be able to recognize any member of a category of objects in spite of wide variations in visual appearance due to variations in the form and colour of the object, occlusions, geometrical transformations (such as scaling and rotation), changes in illumination, and potentially non-rigid deformations of the object itself. In...
Vardar Acar, Ceren (null; 2018-04-30)
In this study, we mainly propose an algorithm to generate correlated random walk converging to fractional Brownian motion, with Hurst parameter, H∈ [1/2,1]. The increments of this random walk are simulated from Bernoulli distribution with proportion p, whose density is constructed using the link between correlation of multivariate Gaussian random variables and correlation of their dichotomized binary variables. We prove that the normalized sum of trajectories of this proposed random walk yields a Gaussian p...
A Comparative Study on Two Different Direct Parallel Solution Strategies for Large-Scale Problems
Bahcecioglu, T.; Ozmen, S.; Kurç, Özgür (2009-04-08)
This paper presents a comparative study on two different direct parallel solution strategies for the linear solution of large scale actual finite element models: global and domain-by-domain. The global solution strategy was examined by utilizing the parallel multi-frontal equation solver, MUMPS [1], together with a finite element program. In a similar manner a substructure based parallel solution framework [2] was utilized for investigating the domain-by-domain strategy. Various large-scale structural model...
A Modified Parallel Learning Vector Quantization Algorithm for Real-Time Hardware Applications
Alkim, Erdem; AKLEYLEK, SEDAT; KILIÇ, ERDAL (2017-10-01)
In this study a modified learning vector quantization (LVQ) algorithm is proposed. For this purpose, relevance LVQ (RLVQ) algorithm is effciently combined with a reinforcement mechanism. In this mechanism, it is shown that the proposed algorithm is not affected constantly by both relevance-irrelevance input dimensions and the winning of the same neuron. Hardware design of the proposed scheme is also given to illustrate the performance of the algorithm. The proposed algorithm is compared to the corresponding...
Citation Formats
İ. Ulusoy, “A generative model for multi class object recognition and detection,” ARTIFICIAL INTELLIGENCE AND NEURAL NETWORKS, pp. 32–40, 2006, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52573.