A connectionist model for category perception: theory and implementation
TL;DR: A connectionist model for learning and recognizing objects (or object classes) is presented and the theory of learning is developed based on some probabilistic measures.
Abstract: A connectionist model for learning and recognizing objects (or object classes) is presented. The learning and recognition system uses confidence values for the presence of a feature. The network can recognize multiple objects simultaneously when the corresponding overlapped feature train is presented at the input. An error function is defined, and it is minimized for obtaining the optimal set of object classes. The model is capable of learning each individual object in the supervised mode. The theory of learning is developed based on some probabilistic measures. Experimental results are presented. The model can be applied for the detection of multiple objects occluding each other. >
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TL;DR: It is observed that HSs of patients are successfully classified by the GAL network compared to the LVQ network.
155 citations
01 Mar 1994
TL;DR: Two connectionist models for mid-level vision problems, namely, edge and line linking, have been presented and the experimental results and the proof of convergence of the network models have been provided.
Abstract: In this paper two connectionist models for mid-level vision problems, namely, edge and line linking, have been presented. The processing elements (PE) are arranged in the form of two-dimensional lattice in both the models. The models take the strengths and the corresponding directions of the fragmented edges (or lines) as the input. The state of each processing element is updated by the activations received from the neighboring processing elements. In one model, each neuron interacts with its eight neighbors, while in the other model, each neuron interacts over a larger neighborhood. After convergence, the output of the neurons represent the linked edge (or line) segments in the image. The first model directly produces the linked line segments, while the second model produces a diffused edge cover. The linked edge segments are found by finding out the spine of the diffused edge cover. The experimental results and the proof of convergence of the network models have also been provided. >
62 citations
TL;DR: A neural network with a multilayer perceptron (MLP) structure as the base learning model is used and results show the effectiveness of this method in various video stream data sets.
Abstract: This paper proposes an incremental multiple-object recognition and localization (IMORL) method. The objective of IMORL is to adaptively learn multiple interesting objects in an image. Unlike the conventional multiple-object learning algorithms, the proposed method can automatically and adaptively learn from continuous video streams over the entire learning life. This kind of incremental learning capability enables the proposed approach to accumulate experience and use such knowledge to benefit future learning and the decision making process. Furthermore, IMORL can effectively handle variations in the number of instances in each data chunk over the learning life. Another important aspect analyzed in this paper is the concept drifting issue. In multiple-object learning scenarios, it is a common phenomenon that new interesting objects may be introduced during the learning life. To handle this situation, IMORL uses an adaptive learning principle to autonomously adjust to such new information. The proposed approach is independent of the base learning models, such as decision tree, neural networks, support vector machines, and others, which provide the flexibility of using this method as a general learning methodology in multiple-object learning scenarios. In this paper, we use a neural network with a multilayer perceptron (MLP) structure as the base learning model and test the performance of this method in various video stream data sets. Simulation results show the effectiveness of this method.
52 citations
Cites background from "A connectionist model for category ..."
...In [5], a connectionist model for recognizing multiple objects was presented....
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TL;DR: The relevance of integration of the merits of fuzzy set theory and neural network models for designing an efficient decision making system is explained and feasibility of such systems and different ways of integration are described.
Abstract: The relevance of integration of the merits of fuzzy set theory and neural network models for designing an efficient decision making system is explained. The feasibility of such systems and different ways of integration, so far made, in the context of image processing and pattern recognition are described. Scope for further research and development is outlined. An extensive bibliography is also provided.
27 citations
TL;DR: A connectionist system has been designed for learning and simultaneous recognition of flat industrial objects by integrating the psychological hypotheses with the generalized Hough transform technique, which uses the mechanism of selective attention for initial hypotheses generation.
Abstract: A connectionist system has been designed for learning and simultaneous recognition of flat industrial objects (based an the concepts of conventional and structured connectionist computing) by integrating the psychological hypotheses with the generalized Hough transform technique. The psychological facts include the evidence of separation of two regions for identification ("what it is") and pose estimation ("where it is"). The system uses the mechanism of selective attention for initial hypotheses generation. A special two-stage training paradigm has been developed for learning the structural relationships between the features and objects and the importance values of the features with respect to the objects. The performance of the system has been demonstrated on real-life data both for single and mixed (overlapped) instances of object categories. The robustness of the system with respect to noise and false alarming has been theoretically investigated. >
24 citations
References
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01 Apr 1989
TL;DR: A competition-based connectionist model for solving diagnostic problems is described, which settles into a locally optimal state in which some disorder nodes are fully activated and compose the diagnosis for the given case, while all other disorder node are fully deactivated.
Abstract: A competition-based connectionist model for solving diagnostic problems is described. The problems under consideration are computationally difficult in that multiple disorders may occur simultaneously and a global optimum in the space exponential to the total number of possible disorders is sought as a solution. To solve this problem, global optimization criteria are decomposed into local optimization criteria that are used to govern node activation updating in the connectionist model. Nodes representing disorders compete with each other to account for each 'individual' present manifestation, yet complement each other to account for all present manifestation, yet complement each other to account for all present manifestations through parallel node interactions. When equilibrium is reached, the network settles into a locally optimal state in which some disorder nodes (winners) are fully activated and compose the diagnosis for the given case, while all other disorder nodes are fully deactivated. A resettling process is proposed to improve accuracy. Three randomly generated examples of diagnostic problems, each of which has 1024 cases, were tested. >
139 citations
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01 Oct 1990
TL;DR: The authors concentrate here on connectionism's potential as a practical technology for building intelligent systems, and also some of the unsolved problems facing this approach.
Abstract: A number of researchers have begun exploring the use of massively parallel architectures in an attempt to get around the limitations of conventional symbol processing. Many of these parallel architectures are connectionist: The system's collection of permanent knowledge is stored as a pattern of connections or connection strengths among the processing elements, so the knowledge directly determines how the processing elements interact rather that sitting passively in a memory, waiting to be looked at by the CPU. Some connectionist schemes use formal, symbolic representations, while others use more analog approaches. Some even develop their own internal representations after seeing examples of the patterns they are to recognize or the relationships they are to store. Connectionism is somewhat controversial in the AI community. It is new, still unproven in large-scale practical applications, and very different in style from the traditional AI approach. The authors have only begun to explore the behavior and potential of connectionist networks. In this article, the authors describe some of the central issues and ideas of connectionism, and also some of the unsolved problems facing this approach. Part of the motivation for connectionist research is the possible similarity in function between connectionist networks and the neutral networksmore » of the human cortex, but they concentrate here on connectionism's potential as a practical technology for building intelligent systems.« less
136 citations
TL;DR: Connectionism is somewhat controversial in the AI community as mentioned in this paper, and it is still unproven in large-scale practical applications, and very different in style from the traditional AI approach.
Abstract: A number of researchers have begun exploring the use of massively parallel architectures in an attempt to get around the limitations of conventional symbol processing. Many of these parallel architectures are connectionist: The system's collection of permanent knowledge is stored as a pattern of connections or connection strengths among the processing elements, so the knowledge directly determines how the processing elements interact rather that sitting passively in a memory, waiting to be looked at by the CPU. Some connectionist schemes use formal, symbolic representations, while others use more analog approaches. Some even develop their own internal representations after seeing examples of the patterns they are to recognize or the relationships they are to store. Connectionism is somewhat controversial in the AI community. It is new, still unproven in large-scale practical applications, and very different in style from the traditional AI approach. The authors have only begun to explore the behavior and potential of connectionist networks. In this article, the authors describe some of the central issues and ideas of connectionism, and also some of the unsolved problems facing this approach. Part of the motivation for connectionist research is the possible similarity in function between connectionist networks and the neutral networksmore » of the human cortex, but they concentrate here on connectionism's potential as a practical technology for building intelligent systems.« less
131 citations