Research Article
a
October
2017
Computer Science and Software Engineering
ISSN: 2277-128X (Volume-7, Issue-10)
Perception Based Object Recognition Using SP Theory
Mapreet Kaur, Simarjot Kaur
Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib, Punjab, India
Abstract— Improved SP theory is an approach for extracting distinctive invariant features from images. It has been successfully applied to a variety of computer vision problems based on feature matching including object recognition, pose estimation, image retrieval and many others. The main idea is to divide the features extracted from both the test and the model object image into several sub-collections before they are matched. The features are divided into several sub-collections considering the features arising from different octaves that are from different frequency domains. This study provides a preliminary, concise, but complete background of the object detection problem. Problems like more energy consumption, bulkiness, less sophisticated for parallel streams of data and more elapsed time occurred, to resolve this issue combination of IPSO-GA has been used. Thus, based on this study, for a given problem environment and data availability, a proper framework can be chosen easily and quickly. In this research the accuracy and the capability to detect the object in the noisy medium is achieved.
Keywords— Sp theory, Object detection
I. INTRODUCTION
The SP theory of intelligence and its realisation in the SP machine may facilitate the development of autonomous robots: by increasing the computational efficiency of computers; by facilitating the development of human-like versatility in intelligence; and likewise for human-like adaptability in intelligence. With regard to the first problem, the SP system has potential for substantial gains in computational efficiency, with corresponding cuts in energy consumption and in the bulkiness of computing machinery: by reducing the size of data to be processed; by exploiting statistical information that the system gathers as an integral part of how it works; and via an updated version of Hebb's concept of a ``cell assembly''.
In the quest for human-like versatility in intelligence, the SP system has strengths in several areas including unsupervised learning, natural language processing, pattern recognition, information retrieval, several kinds of reasoning, planning, problem solving, and more, with seamless integration amongst structures and functions. The SP system may also promote human-like adaptability in intelligence via its strengths in unsupervised learning. These capabilities may underpin one-trial learning, the learning of natural language, learning to see, building 3D models of objects and of a robot's surroundings, learning how a robot interacts with its environment and other regularities, learning minor skills via exploration and play, learning major skills, and learning via demonstration. AI is the intelligence exhibited by machines or software. It is also the name of the academic field of study which studies how to create computers and computer software that are capable of intelligent behaviour. General intelligence is still among the field's long-term goals. Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are a large number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others. The AI field is interdisciplinary, in which a number of sciences and professions converge, including computer science, mathematics, psychology, linguistics, philosophy and neuroscience, as well as other specialized fields such as artificial psychology. Artificial Intelligence is an area which emphasizes on generating intelligent machines that work and reacts like humans. It is also the name of the acade
The central problems of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[1] General intelligence is still among the field's long-term goals. Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are a large number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others. The AI field is interdisciplinary, in which a number of sciences and professions converge, including computer science, mathematics, psychology, linguistics, philosophy and neuroscience, as well as other specialized fields such as artificial psychology.
II. SPTHEORYOFINTELLIGENCE
ISSN(E): 2277-128X, ISSN(P): 2277-6451, pp. 16-21
via multiple alignment as a unifying theme. It is realised in the SP computer model which may be regarded as an early version of the SP machine.
The main elements of SP theory
1. All knowledge is expressed as patterns.
2. All processing is done by compression of information. 3. Probabilities may be calculated.
4. „Multiple alignment‟ is a powerful central idea. 5. The SP theory realised in the SP70 computer model.
6. Patterns may be realised in a modified version of Hebb‟s cell assembly concept.
An important part of this process is, where possible, the economical (compressed) encoding of New patterns in terms of Old patterns. This may be seen to achieve such things as pattern recognition, parsing or understanding of natural language, or other kinds of interpretation of incoming information in terms of stored knowledge, including several kinds of reasoning.
This paper is regarding however the SP theory of intelligence and its realization within the SP machine could facilitate within the style of the information-processing „brains‟ of autonomous robots. Here, „autonomous robots‟ are ones that don't rely on external intelligence (natural or artificial), don't rely on external power provides, and are mobile. We tend to shall additionally assume that a goal in their development is to produce them with human-like skillfulness and adaptableness
in intelligence.
The paper has relevancy to robots that don't seem to be autonomous within the sense simply represented, however the issues to be addressed are most acute in robots that are supposed to perform autonomously and potential solutions are correspondingly additional attention-grabbing. There are number of merits of using SP theory these are given as follows:
Fig 1: Schematic representation of the development and application of the SP machine.[1]
III. EVOLUTIONARY ALGORITHM
A genetic algorithm (GA) is a method for solving both constrained and unconstrained optimization problems based on a natural selection process that mimics biological evolution. The algorithm repeatedly modifies a population of individual solutions. At each step, the genetic algorithm randomly selects individuals from the current population and uses them as parents to produce the children for the next generation. Over successive generations, the population "evolves" toward an optimal solution.
You can apply the genetic algorithm to solve problems that are not well suited for standard optimization algorithms, including problems in which the objective function is discontinuous, non-differentiable, stochastic, or highly nonlinear.
Genetic Algorithm parameters
ISSN(E): 2277-128X, ISSN(P): 2277-6451, pp. 16-21
Selection: It is the phase where individual patterns with better fitness are selected to discover best solution. Crossover: It is a process where each pair of individuals selects randomly participates in exchanging their
parents with each other, until a total new population has been generated.
Mutation: It is random changing of parameters in some of some of the surviving strings. It must be low as if it‟s too low the algorithm will not cover the search space much. if its too high then good candidate solutions will be perturbed and pushed into worse solutions.
Advantages of GA
GA has been used to evolve computer programs for specific tasks and to design other computational structures. Due to the parallelism that allows them to implicitly evaluate many schemas at once.
It can solve problems with multiple solutions.
Genetic algorithms are easily transferred to existing simulations and models.
Since the Genetic algorithm execution technique is not dependent on the error surface, it can be used for multi-dimensional, non-differential, non-continuous and even non-parametrical problems.
Structural Genetic algorithm gives us the possibility to solve the solution structure and solution parameter problems at the same time by means of Genetic algorithm.
The genetic algorithm differs from a classical, derivative-based, optimization algorithm in two main ways, as summarized in the following table.
Table 1: Comparison of Classic Algorithm and Evolutionary Algorithm
Classic Algorithm Evolutionary Algorithm
Generates a single point at each iteration. The sequence of points approaches an optimal solution.
Generates a population of points at each iteration. The best point in the population approaches an optimal solution.
Selects the next point in the sequence by a deterministic computation.
Selects the next population by computation which uses random number generators.
Fig 2: Flow Chart Start
Load image
Pre process captured Image
Extract feature set
Implement SP-neural network using EA
Feed Tested Images
Compare extracted Features
Generate and validate results
ISSN(E): 2277-128X, ISSN(P): 2277-6451, pp. 16-21
Algorithm for Improved SP Theory: Step 1: Begin
Step 2: Load Image Data set.
Step 3: Filter image data set and uniform intensity.
Step 4: Extract features of loaded image.
Step5: Initialise count = 0 for number of features
Step 6: Input extracted features in EA.
Step 7: Initialize i=0
Step 8: while i<count(total features)
Step 9: Select required feature set. (At time of feature extraction for eg. Geometric features or glcm based features, we don‟t need the whole set of features, so using the GA algorithm, i.e. a stack based approach we select only the required ones.)
Step 10: end
Step 11: Input the output list (output of EA) to IPSO.
Step 14: End
Step 15: After the IPSO processing of selected features a count will be generated from which it can be determined that the tested dataset image has any match with the training data set.
Step 16: If count >threshold
Step 17: Perception successful.
Step 18: else
Step 19: Image does not match with the training image data set.
Step 20: End
IV. RESULTS
Fig 3 is the representation of pre final steps in the research proposal in which the locations are matched according to the extracted features of training and tested images.
Fig 3: Location Matching
Fig 4 is depicting about the number of locations that are matched in this algorithm. From this it is clear that the accuracy in the proposed algorithm is far better than that of existing algorithm, as it is finding out more number of features and location in less time. So the proposed algorithm is better than that of existing in both way time and accuracy.
ISSN(E): 2277-128X, ISSN(P): 2277-6451, pp. 16-21
From the figure it is clear that our proposed algorithm is finding out the location in both the images and then compares it if these are matched or not.
Improved SP theory
Improved SP theory is an approach for extracting distinctive invariant features from images. It has been successfully applied to a variety of computer vision problems based on feature matching including object recognition, pose estimation, image retrieval and many others. The main idea is to divide the features extracted from both the test and the model object image into several collections before they are matched. The features are divided into several sub-collections considering the features arising from different octaves that are from different frequency domains. This study provides a preliminary, concise, but complete background of the object detection problem.
Threshold value may be defined as a value with which the feature values may be compared to and find out results. Using the threshold value the image can be compared to dataset images to check the features value matching. Threshold value is set to 90% in this proposed scenario so the accuracy may be improved.
Table 1. Comparison of SP theory and improved SP theory for matching points
Image No.
Database image
SP Theory (2.5)
SP Theory (2.5)
Improved SP (4.2) Improved SP (4.2)
Matching Points Time Matching Points Time
1 Dbi 1 178 0.0336 187 0.024
2 Dbi 2 198 0.063 207 0.045
3 Dbi 3 154 0.0602 176 0.043
4 Dbi 4 176 0.056 200 0.04
5 Dbi 5 160 0.050909091 184 0.036363636
V. CONCLUSION
This research addresses many major aspects of an object detection framework. These include feature selection, learning model, object representation, matching features and object templates, and the boosting schemes. Improved SP theoryis an approach for extracting distinctive invariant features from images. It has been successfully applied to a variety of computer vision problems based on feature matching including object recognition, pose estimation, image retrieval and many others. From the results given above, it is clear that the proposed approach is better than that of existing scheme. Background codes/theories can be used from the works cited here relevant to the chosen framework and the focus of research can be dedicated to improving or optimizing the chosen framework for better accuracy in the given problem environment.
REFERENCES
[1] James Gerard Wolff, "Autonomous Robots and the SP Theory of Intelligence", IEEE, ISSN: 2169-3536, 2015. [2] Xiaofei Wang, Xiuhua Li, Victor C. M. Leung, "Artificial Intelligence-Based Techniques for Emerging
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ISSN(E): 2277-128X, ISSN(P): 2277-6451, pp. 16-21
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