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Histogram based color pattern identification of multiclass fruit using feature selection
Rachmawati E.a, Khodra M.L.a, Supriana I.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2015 IEEE.Color histogram has been widely used in feature extraction to represent color feature of an object in the image. In this paper, we identify which features that give high contribution in classification performance, because not all features are directly correlated with object category. In the case of n-bins color histogram, features were referred to color intensity range of color histogram. On the one hand, we consider fruit classification, where the feature space contains various properties of pixel intensities of RGB (Red-Green-Blue) channel. On selecting feature subset, we consider filter method of feature selection. In the filter method, we successively reduce the size of the feature sets and investigate the changes in the classification results. Specifically, we followed the filtering approach to feature selection: selecting features in a single pass first and then applying a classification algorithm independently. We used chi square feature selection to determine relevant features from RGB histogram. Further, we used and evaluated those relevant features in a classification system, using K-Nearest Neighbor (KNN) as classifier. In this paper we show that by conducting feature selection techniques combined with KNN we would be able to prune non-relevant intensities value of Red, Green, and Blue channel. Furthermore, we use the relevant subset of features to identify intensities range of RGB channel that was needed to represent 32 subcategories fruit image efficiently.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Classification algorithm,Classification performance,Classification results,Classification system,Color histogram,Color pattern,K nearest neighbor (KNN),K-nearest neighbors[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]chi-square feature selection,color histogram,color pattern,K-Nearest Neighbor[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICEEI.2015.7352467[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]