Identification Of Image Targets

Image recognition technology has a very broad application space. Some years, the sparse representation based identification method is becoming a hot field of image recognition. This paper studies the existing algorithms, proposed several improvements sparse expression recognition algorithm, the feasibility of an infrared image recognition algorithm is applied.Firstly, this paper studied the classical sparse representations and conventional dictionary learning method, on this basis, the simulation study based on sparse representation of face recognition dictionary learning method. Use a dictionary learning algorithm (K-SVD), and better parameters through experiments, with this dictionary learning algorithm can reduce the dictionary reached on the basis of maintaining the recognition rate on the effect of reducing the computational complexity.Then study related categories based on neighbor subspace maximum likelihood sparse representation recognition algorithm. An improved adaptive parameters neighbor classification principles proposed. Through this adaptive method to select samples from each category adaptive number of local neighborhood constitute a new dictionary, the composition of the new dictionary, and then based on the maximum likelihood representation model for image recognition. Simulation results show that the algorithm can effectively improve the recognition rate, through simulation experiments can be proved that this algorithm also has a certain robustness. However, this algorithm itself high computational complexity, and did not use validated on infrared data..Furthermore, this paper analyzes the characteristics of atomic structure sparse and sparse two guidelines proposed an atomic structure sparse and sparse combination of methods, mainly studied the parallel and serial two ways. The parallel weighted algorithm is improved to use a small number of samples adaptive solution the optimal value, the calculation method of serial combination is to sequential use of atomic sparse and structure sparse standards. Through the face database and a small amount of infrared data validation, verify the effectiveness of the algorithm, and can be used in infrared data.