Content Based Image Retrieval For Identification of Plants: Using Color, Texture and Shape Features

 
9783847310846: Content Based Image Retrieval For Identification of Plants: Using Color, Texture and Shape Features
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In this book, an application of content-based image retrieval is proposed for plant identification, along with a preliminary implementation. The system takes a plant image as input and finds the matching plant from a plant image database and is intended to provide users a simple method to locate information about their plants. With a larger database, the implemented system might be used by biologists, as an easy way to access to plant databases. As preprocessing for identification, segmentation of the input image is proposed to extract the general structure of the plant. Various color, texture and shape features extracted from the segmented plant region are used in matching images to the database. Color and texture analysis are based on commonly used features, while for shape, some new descriptors are introduced to capture the outer contour characteristics of a plant. Results show that for 54% of the queries, the correct plant image is retrieved among the top-15 results, using our database of 380 plants from 78 different plant types. The test results obtained using a cleaner database increased the top-15 retrieval probability to 68%.

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About the Author:

Hanife Kebapci is a MSc. Computer Engineer, achieved her Master of Science degree in 2009 presenting her thesis, prepared in Sabanci University, Istanbul, on Content Based Image Retrieval. Published some articles on the same topic as well. From 2009, she is now working as a software engineer specializing on Java technologies.

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Book Description Condition: New. Publisher/Verlag: LAP Lambert Academic Publishing | Using Color, Texture and Shape Features | In this book, an application of content-based image retrieval is proposed for plant identification, along with a preliminary implementation. The system takes a plant image as input and finds the matching plant from a plant image database and is intended to provide users a simple method to locate information about their plants. With a larger database, the implemented system might be used by biologists, as an easy way to access to plant databases. As preprocessing for identification, segmentation of the input image is proposed to extract the general structure of the plant. Various color, texture and shape features extracted from the segmented plant region are used in matching images to the database. Color and texture analysis are based on commonly used features, while for shape, some new descriptors are introduced to capture the outer contour characteristics of a plant. Results show that for 54% of the queries, the correct plant image is retrieved among the top-15 results, using our database of 380 plants from 78 different plant types. The test results obtained using a cleaner database increased the top-15 retrieval probability to 68%. | Format: Paperback | Language/Sprache: english | 96 pp. Seller Inventory # K9783847310846

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Book Description LAP Lambert Academic Publishing Jan 2012, 2012. Taschenbuch. Condition: Neu. Neuware - In this book, an application of content-based image retrieval is proposed for plant identification, along with a preliminary implementation. The system takes a plant image as input and finds the matching plant from a plant image database and is intended to provide users a simple method to locate information about their plants. With a larger database, the implemented system might be used by biologists, as an easy way to access to plant databases. As preprocessing for identification, segmentation of the input image is proposed to extract the general structure of the plant. Various color, texture and shape features extracted from the segmented plant region are used in matching images to the database. Color and texture analysis are based on commonly used features, while for shape, some new descriptors are introduced to capture the outer contour characteristics of a plant. Results show that for 54% of the queries, the correct plant image is retrieved among the top-15 results, using our database of 380 plants from 78 different plant types. The test results obtained using a cleaner database increased the top-15 retrieval probability to 68%. 96 pp. Englisch. Seller Inventory # 9783847310846

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Book Description LAP Lambert Academic Publishing Jan 2012, 2012. Taschenbuch. Condition: Neu. Neuware - In this book, an application of content-based image retrieval is proposed for plant identification, along with a preliminary implementation. The system takes a plant image as input and finds the matching plant from a plant image database and is intended to provide users a simple method to locate information about their plants. With a larger database, the implemented system might be used by biologists, as an easy way to access to plant databases. As preprocessing for identification, segmentation of the input image is proposed to extract the general structure of the plant. Various color, texture and shape features extracted from the segmented plant region are used in matching images to the database. Color and texture analysis are based on commonly used features, while for shape, some new descriptors are introduced to capture the outer contour characteristics of a plant. Results show that for 54% of the queries, the correct plant image is retrieved among the top-15 results, using our database of 380 plants from 78 different plant types. The test results obtained using a cleaner database increased the top-15 retrieval probability to 68%. 96 pp. Englisch. Seller Inventory # 9783847310846

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Book Description LAP Lambert Academic Publishing. Paperback. Condition: New. 96 pages. Dimensions: 8.6in. x 5.9in. x 0.4in.In this book, an application of content-based image retrieval is proposed for plant identification, along with a preliminary implementation. The system takes a plant image as input and finds the matching plant from a plant image database and is intended to provide users a simple method to locate information about their plants. With a larger database, the implemented system might be used by biologists, as an easy way to access to plant databases. As preprocessing for identification, segmentation of the input image is proposed to extract the general structure of the plant. Various color, texture and shape features extracted from the segmented plant region are used in matching images to the database. Color and texture analysis are based on commonly used features, while for shape, some new descriptors are introduced to capture the outer contour characteristics of a plant. Results show that for 54 of the queries, the correct plant image is retrieved among the top-15 results, using our database of 380 plants from 78 different plant types. The test results obtained using a cleaner database increased the top-15 retrieval probability to 68. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Seller Inventory # 9783847310846

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Book Description LAP Lambert Academic Publishing, Germany, 2012. Paperback. Condition: New. Aufl.. Language: English . Brand New Book. In this book, an application of content-based image retrieval is proposed for plant identification, along with a preliminary implementation. The system takes a plant image as input and finds the matching plant from a plant image database and is intended to provide users a simple method to locate information about their plants. With a larger database, the implemented system might be used by biologists, as an easy way to access to plant databases. As preprocessing for identification, segmentation of the input image is proposed to extract the general structure of the plant. Various color, texture and shape features extracted from the segmented plant region are used in matching images to the database. Color and texture analysis are based on commonly used features, while for shape, some new descriptors are introduced to capture the outer contour characteristics of a plant. Results show that for 54 of the queries, the correct plant image is retrieved among the top-15 results, using our database of 380 plants from 78 different plant types. The test results obtained using a cleaner database increased the top-15 retrieval probability to 68 . Seller Inventory # KNV9783847310846

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