NDLI: Where-what network-4: The effect of multiple internal areas

Content Provider	IEEE Xplore Digital Library
Author	Luciw, M. Juyang Weng
Copyright Year	2010
Description	Author affiliation: Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, 48824 (Luciw, M.; Juyang Weng)
Abstract	The general visual attention-recognition (AR) problem remains open. Given a set of images, each with a single target foreground over some complex background, it requires output of both location and type of this single foreground. First, many approaches cannot deal with the richness of the class of possible backgrounds, which has a huge number of variations and also could include distractor-like patterns. This potentially leads to an explosion of resources required within the model. Second, all current approaches break down as the number of locations, types, and variations (within each type) increases towards human-level. This paper is concerned with model selection for networks dealing with the general AR problem. The major challenge is ensuring the model remains as simple as possible as the complexity of the data increases. In developmental general AR, the model must be adapted on the fly. We discuss these issues in context of the latest version of the biologically-inspired developmental Where-What Network. We show how local detectors reduce the number of neurons exponentially and deal with the complex background problem. The purpose of multiple layers seems to be to allow combinatorial patterns to emerge. Top-down connections cause more discriminative features to develop, but since complex data requires a bank of shared features, top-down connections are probably not beneficial for the early layer(s). When a layer's features are class-specific and there is no combinatorial structure to exploit on top of this layer, it is not useful to add another layer but it is useful to utilize top-down connections to develop more discriminative features.
Starting Page	311
Ending Page	316
File Size	1105107
Page Count	6
File Format	PDF
ISBN	9781424469000
e-ISBN	9781424469024
DOI	10.1109/DEVLRN.2010.5578824
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-18
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Visualization Neurons Retina Entropy Pixel Testing
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Where-what network 1: “Where” and “what” assist each other through top-down connections

Synapse maintenance in the Where-What Networks

2010 ieee 9th international conference on development and learning 311 where-what network-4: the effect of multiple internal areas.

One-dimensional Grey-level Co-occurrence Matrices for texture classification

Modeling the effects of neuromodulation on internal brain areas: Serotonin and dopamine

Visual Interpretation of Self Organizing Maps

Stereo correspondence using Hopfield network with multiple constraints

Random ferns for multiple target tracking in microscopic retina image sequences

Internal fault classification using Artificial Neural Network

Where-what network-4: The effect of multiple internal areas

Similar Documents

Where-what network 1: “Where” and “what” assist each other through top-down connections

Synapse maintenance in the Where-What Networks

2010 ieee 9th international conference on development and learning 311 where-what network-4: the effect of multiple internal areas.

One-dimensional Grey-level Co-occurrence Matrices for texture classification

Modeling the effects of neuromodulation on internal brain areas: Serotonin and dopamine

Visual Interpretation of Self Organizing Maps

Stereo correspondence using Hopfield network with multiple constraints

Random ferns for multiple target tracking in microscopic retina image sequences

Internal fault classification using Artificial Neural Network

Where-what network-4: The effect of multiple internal areas