Object Recognition in Clutter Color Images using Hierarchical Temporal Memory combined with Salient-Region Detection

Radoslav Skoviera, Ivan Bajla, and Julia Kucerova


machine learning, pattern recognition, hierarchical temporal memory, image saliency


The essential goal of this paper consists in extending the functionality of the bio-inspired intelligent HTM (Hierar- chical Temporal Memory) network towards two capabili- ties: (i) object recognition in color images, and (ii) classifi- cation of objects located in "clutter color images. The for- mer extension is based on development of a novel scheme for application of three parallel HTM networks which sepa- rately process color, texture, and shape information in color images. For the latter HTM extension we proposed a novel system in which HTM is combined with a modified model of computational visual attention. We adopted the results of [1] and [2], and added new elements [3] for the calcula- tion of image saliency maps. The proposed algorithm en- ables to locate individual objects in clutter images automat- ically. For computer experiments a special image database has been created to simulate ideal single object images and cluttered images with multiple objects. The recognition performance of the HTM alone and in combination with a salient-region detection method has been evaluated. The evaluation of the attention subsystem shows promising re- sults in the sense that the system satisfactorily locates sev- eral objects in clutter color images with non-homogeneous background. Our pilot study confirmed that the proposed attention system can improve the HTM’s capabilities for object classification in cluttered images. However, as ex- pected, the system cannot match the HTM’s recognition accuracy achieved on single object images.

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