Em relação ao desenvolvimento de trabalhos futuros, sugere-se a aplicação da técnica proposta em outras bases de dados, tanto de reconhecimento facial, como de reco- nhecimento de cenas e reconhecimento de objetos, a fim de avaliar sua eficiência para esses
68 Capítulo 5. Conclusão
casos. Além disso, outras combinações de parametrização de – podem ser desenvolvidas com o objetivo de melhorar significativamente o resultado do K-SVD quando o valor de esparsidade L é alto. Uma vez que o processo de parametrização pode ser dispendioso, recomenda-se a investigação de meta-heurísticas ou técnicas automáticas para esse fim. Uma possibilidade, nesse sentido, seria a implementação de um Algoritmo Genético para encontrar combinações adequadas para o vetor –, ou, até mesmo, para otimizar a função objetivo do problema de aprendizado de dicionário.
De forma análoga ao realizado nesse trabalho, pode-se desenvolver estratégias es- pecíficas para o aperfeiçoamento da taxa de classificação, ao contrário do erro de recupe- ração. Uma ideia que pode ser explorada, nesse contexto, é o “descarte de colunas”. Uma vez que o dicionário é ajustado no processo de aprendizado de dicionário, sabe-se que as representações esparsas xi de cada elemento do conjunto de treino está associada a um
conjunto de até L colunas da matriz D. O rótulo de cada vetor xi também é conhecido,
e, portanto, é possível relacionar as colunas do dicionário D com as classes do conjunto de treino. Lança-se como hipótese que, para construir uma representação esparsa mais discriminante, pode-se renovar as colunas que são utilizadas por elementos de treino de classes diferentes. Presume-se que colunas utilizadas por elementos de diversas classes não guardam informações relevantes sobre uma classe específica, logo essas colunas pode- riam ser removidas da representação esparsa. Dessa forma, perder-se-ia a capacidade de recuperar informação, mas conjectura-se que uma melhora na taxa de classificação será observada.
69
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