A Generic Framework for Feature Representations in Image Categorization Tasks

The Visual Feature Array Concept

At the heart of the proposed concept is a Visual Feature Array (VFA) model, which is a cognitive information processing model that uses the information processing structures defined in the VFA concept. The VFA model obtains information from the environment, performs various operations on it, and then supplies higher-order models of cognitive informatics with its output. The proposed VFA concept allows for the implementation of cognitive functions analogous to those performed in the primary visual cortex (Figure 1).

Figure 1.

The VFA concept

Information Processing Structures in the VFA Concept

The information processing structures of the VFA concept describe the units available and their relationships in constructing a VFA model. VFA models can be composed of data arrays and operations that can be performed on them. The models constructed in the VFA concept are functionally similar to the information processing structures in the brain, where a large number of neurons (data arrays in the concept) affect the output of other large numbers of neurons, according to the topology and strength of their connections (operations in the concept).

Data Arrays

The data arrays are multidimensional arrays whose values can be of any type. The arrays were conceived to represent the output of individual neurons responsible for visual features in the modeled neural structures. The values contained in data arrays will be subsequently referred to as (artificial) neurons or computational elements.

Operations

An operation takes a data array of dimensions as its operand, and places its result in a data array of dimensions. The operations of the VFA concept are of SIMD type, and each of them can have static and / or running parameters. Running parameters assume discrete values taken from a limited interval.

Let denote an operation, where is the number of running parameters. Let denote the input data array, and the output data array, where and are the dimensions of and respectively. The performed operation can then be written as.

Three basic operation types are defined:

• ● Filtering operations

• ● Lateral operations

• ● Projective operations