978-1-4799-3351-8/14/$31.00 ©2014 IEEE A Cortex-like Model For Animal Recognition Based On Texture Using Feature-Selective Hashing Sahar Seifzadeh Department Of Electrical,Computer And Biomedical Engineering, Qazvin Branch ,Islamic University ,Qazvin,Iran Seifzadeh.sahar5@yahoo.com Sahar.seifzadeh1986@gmail.com Karim Faez Electrical Engineering Department, AmirKabir University of Technology Tehran, Iran kfaez@aut.ac.ir Abstract— Building a model that can mimic the brain's cortex has always been a major goal, because the human brain recognizes objects in terms of speed, reliability and flexibility that are always unique pattern for machine vision systems. In this paper, we are inspired by neuroscience and computer science that have designed a framework that can be fast and accurate emulation of the inferior temporal cortex with feature selective hashing to recognize animals. We worked on KTH database containing 1239 images in 13 classes that took photos from animals in wild. Keywords: Cortex-like model; animal recognition; textural fearure extraction; feature-selective Hashing I. INTRODUCTION Human ability in vision always is a great pattern for machine vision systems. Research shows that it takes 12.5 milliseconds to detect an object in the monkey’s brain. In computer science when we have a big data for finding a nearest neighborhood typically, the data are categorized by constructing decision tree or hashed table. This is to avoid wasting time. But in the neurosciences, neurons that give the same answer to a specific object are categorized in same category. This Framework are constructed by hierarchical feed forward model and the appropriate hash function (feature-selective hashing (FSH)) to recognize animals. The human visual system is illustrated in Figure 1. As you can see visual information through the optic nerve to the visual cortex of the brain, and eventually it reaches the bottom of the cortex or the Inferior Temporal (IT) (Fig.2) that Is responsible for recognizing objects. And in this paper we imitate this part of the cortex for animal recognition with FSH based on texture features. We are inspired by the Yu-Ju Lee et al [1] paper that they contrast the framework based on cortex-model to object recognition and they test their framework on different objects database, While we work on the wild animal’s database by textural feature extractors. Use of these extractors is that animals cannot be categorized based on color or shape, Because of these two aspects they are very similar, And we have a great error rate. The best features which animals can be classified is textural properties or combinations of texture and shape, according to these reasons, we use Segmentation-based Fractal Texture Analysis (SFTA), Haralick texture features and Haar Of Oriented Gradient (HOOG) for extract textural features. We first review the works that we inspired by them in this paper, Then we discuss our proposed method step by step, and finally we examine our experiment to test the efficiency and accuracy of our method. II. BACKGROUND Here, we review previous works that we’re inspired by them to write this paper: Henry et al [2] used a neural network to recognize faces by using intensities in a 20*20 sub-window. Haar wavelet feature has been very popular since the Viola and Jones [3] proposed their real-time face detection system. Their method consists of a cascade classifier with AdaBoost algorithm on a set of features that were trained by Haar wavelet. Dalal and Triggs in 2005 [4], proposed higher human detection system by training a SVM classifier using the histogram of gradient features (HOG). Alceu Ferraz Costa et al [5] presented a paper that proposed a new and efficient texture feature extraction method: SFTA. The extraction algorithm consists in analysis the input image into a set of binary images from which the fractal dimensions of the resulting regions are computed in order to describe segmented texture patterns. The analysis of the input image is achieved by the Two-Threshold Binary Decomposition (TTBD) algorithm, which they also proposed in their work. SFTA uses thresholding as part of its extraction algorithm. However, the input image to the SFTA function does not need to be segmented.