Classification of segmented images combining neural networks and wavelet matching
Leticia Flores-Pulido, Aurelio López‐López, Leopoldo Altamirano-Robles · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
The need for content-based image retrieval is staggering given the information explosion. The advantage of systems for content-based retrieval is their effectiveness to find relevant images when searching in large collections. We believe that an improved technique for retrieval can be achieved by combining techniques. So, we approach the problem in two stages: an organization and retrieval phase, employing different techniques in each. First phase classifies the images based on a preliminary feature extraction progress. This feature extraction gathers a set of statistical parameters. After the extraction, the images are classified employing an ART2 neural network model of 12 inputs and 16 outputs. Adaptive resonance architectures are networks that self-organize stable pattern recognition codes in real-time in response to arbitrary sequences of input patterns. The retrieval phase allows searching and relies on a wavelet matching process. This process works on representative images, and has been successful in information retrieval tasks. The wavelet Haar transform employed can be estimated quickly and tends to produce blocks int eh image details. The wavelet process is applied in L2. A similarity assessment process with an associated measure is necessary for wavelet matching. We report in detail the phases, and the preliminary results already achieved.